Systems and methods for filtering noise and analyzing venous waveform signals

ABSTRACT

Devices, systems, and methods for filtering medical device noise artifacts from venous waveform signals are disclosed. A peripheral venous pressure (PVP) is measured and transformed from the time domain to the frequency domain for analysis to determine patient status. To avoid artifacts of the pumping, the time-domain PVP measurements are filtered to generate a filtered time-domain PVP signal by removing active pumping periods. The filtered time-domain PVP signal is transformed into a frequency-domain PVP signal, which is analyzed based upon peaks indicating respiratory rate, heart rate, or harmonics thereof. A metric of patient status is then determined from the peaks or corresponding frequencies. The patient status may be related to blood volume of the patient and may be used to control pump operation.

PRIORITY CLAIM

This application claims priority to U.S. Provisional Application No.62/671,108, entitled “System and Method for Monitoring and DeterminingPatient Parameters from Sensed Venous Waveform”, filed May 14, 2018,U.S. Provisional Application No. 62/599,421, entitled “Systems andMethods for Filtering Medical Device Noise Artifacts from VenousWaveform Signals”, filed Dec. 15, 2017, U.S. Provisional Application No.62/527,944, entitled “System and Method for Filtering Medical DeviceNoise Artifacts from Venous Waveform Signal”, filed Jun. 30, 2017, andU.S. Provisional Application No. 62/528,570, entitled “System and Methodfor Utilizing Venous Waveform Signal to Identify and/or Assess PatientGait, Seizure, Activity or Other Biometrics”, filed Jul. 5, 2017, theentire contents of which are incorporated herein by reference and reliedupon.

BACKGROUND

Proper patient care requires the determination of a plurality of patientstatus metrics, which are typically measured separately using separateequipment. Measured patient status metrics may be as simple as pulserate or may be more complex, such as patient body temperature or bloodpressure. More complex patient status metrics further includerespiratory volume or blood volume. Although various devices andtechniques exist to measure various patient status metrics, nocomprehensive means of automatically monitoring these various patientmetrics exists. Additionally, some important patient characteristics arenot typically measured, instead being qualitatively assessed by humanobservation. Such unmeasured patient characteristics include patientgait, limp, body position, movement, falls, or ambulatory instability.Both using separate measurement devices and relying upon humanobservation increase system complexity, reduce reliability, and increasecost.

Blood volume metrics are of particular interest because of thecomplexity of their measurement techniques. Conventional methods ofestablishing blood volume and related metrics indicative of patientcondition have relied upon highly invasive measurements of centralvenous pressure (herein “CVP”) or other invasive measures, such asSwan-Ganz catheterization. Such invasive measurements require theinsertion of a catheter specifically for the purpose of measuring bloodpressure within the central portion of the patient's circulatory system.In addition to being highly invasive, the insertion of a catheter solelyfor the purpose of pressure monitoring increases the complexity oftreatment and raises the risk of complications, such as infection.Additionally, CVP measurements may be slower to change in response tocertain acute conditions, as the circulatory system attempts tocompensate for blood volume disequilibrium (particularly hypovolemia) byprotecting blood volume levels in the central circulatory system at theexpense of the periphery. For example, constriction in peripheral bloodvessels may reduce the effect of fluid loss on the central system,thereby masking blood loss for a period of time in conventional CVPmeasurements. Such masking can lead to delayed recognition and treatmentof patient conditions, resulting in worse patient outcomes.

To address the issues associated with CVP measurements, the use ofperipheral intravenous analysis (herein “PIVA”) has been developed, asdescribed in U.S. patent application Ser. No. 14/853,504 (filed Sep. 14,2015 and published as U.S. Patent Publication No. 2016/0073959) and PCTApplication No. PCT/US16/16420 (filed Feb. 3, 2016, and published as WO2016/126856). Such PIVA techniques measure peripheral venous pressure(herein “PVP”) using intravenous (herein “IV”) lines, such as IV tubingattached to a saline drip or IV pump. In addition to utilizing existingIV lines, the PIVA techniques also include transformation of the PVPmeasurements into the frequency domain to identify a respiratory ratefrequency (F₀) equal to the respiratory rate of the patient and a heartrate frequency (F₁) equal to the heart rate of the patient. Although thePIVA techniques previously disclosed provide an excellent indication ofheart rate and blood volume status in certain situations, the disclosureherein further improves upon the previously disclosed PIVA techniques toaddress challenges related to other situations, improve accuracy,provide earlier warnings of potential problems, or identify additionalpatient conditions. Similar problems arise in other conventionalmethods, such as pulmonary artery or capillary pressure measurements.

Monitoring patient metrics during dialysis or other pumping presents aparticular challenge to both conventional and PIVA methods. Inparticular, pumping blood into a patient circulatory system generates ahigh level of (pressure variation induced) noise related to the pumpingcycle. Measured signal values associated with such noise during pumpingperiods may be orders of magnitude larger than signal values associatedwith non-pumping periods. Existing techniques for monitoring patientmetrics under such conditions involve either shutting down the pump foran extended period or attempting to remove the primary effect of thepump from the measured pressure. Shutting down the pump for extendedperiods during treatment may be infeasible where consistent pumping isneeded, such as during surgery. Even where feasible, such approach canstill result in substantial delays in determining the patient statusbecause of the need to interrupt pumping in to obtain measurements.Similarly, existing techniques that attempt to remove the primary effectof the pump address only the principal artifacts introduced by the pumpand are sensitive to errors in estimates of the primary effect of thepump. Such techniques also typically require a priori informationregarding the operation of the pump (e.g., the amplitude and frequencyof pressure waves generated by the pump), and some such techniquesfurther require additional information regarding precise timing of thephases of the pump cycle. Such techniques produce only crude estimatesof pressure, which estimates are unsuitable for PIVA or other advancedmetrics of patient status. Specifically, such techniques at best removeonly approximations of the primary artifacts of pump operation, whileleaving numerous secondary artifacts in the measured pressure signal.Moreover, such techniques are dependent upon accurate estimates of theprimary pumping artifacts and are sensitive to any errors in theestimates, such as errors caused by variation in pump operation overtime. The techniques described herein represent a means of avoiding therespective problems of both types of existing techniques.

Accordingly, systems and methods are needed to filter medical devicenoise artifacts from venous waveform signals.

SUMMARY

In light of the disclosure herein, and without limiting the scope of theinvention in any way, in a first aspect of the present disclosure, whichmay be combined with any other aspect listed herein unless specifiedotherwise, a system for monitoring a patient using a measurementassociated with a peripheral venous pressure (PVP) within a peripheralvein of a circulatory system of the patient while the circulatory systemof the patient is connected to a pump includes a PVP sensor and anevaluation unit. The PVP sensor includes a transducer disposed adjacentto or connected to an intravenous (IV) tube in fluid connection with theperipheral vein. The PVP sensor is configured to generate an electronicsignal associated with the PVP while the circulatory system of thepatient is connected to the pump. The evaluation unit includes acomputer processor communicatively connected to the PVP sensor toreceive the electronic signal and a memory storing non-transitorycomputer-readable instructions that, when executed by the computerprocessor, cause the evaluation unit to obtain a time-domain PVP signalcomprising values of an electronic signal associated with the PVP fromthe transducer based upon a physical phenomenon associated with the PVPof the patient over a sample period. The sample period includes aplurality of time segments, including (i) one or more active timesegments during which the pump is operating and (ii) one or moreinactive time segments during which the pump is not operating. Theevaluation unit identifies a first plurality of the values of thetime-domain PVP signal associated with the one or more inactive timesegments and a second plurality of the values of the time-domain PVPsignal associated with the one or more active time segments, based uponevaluation of the values of the time-domain PVP signal. The evaluationunit generates a filtered time-domain PVP signal based upon the firstplurality of the values and excluding the second plurality of thevalues. The evaluation unit applies a transformation to the filteredtime-domain PVP signal to generate a frequency-domain PVP signal. Theevaluation unit determines a patient status metric for the patient basedupon the frequency-domain PVP signal.

In a second aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the pump is aperistaltic IV pump.

In a third aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the pump isconfigured to operate periodically, such that the one or more activetime segments and the one or more inactive time segments periodicallyalternate.

In a fourth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the IV tubeis disposed between the patient and the pump such that a part of thepump is in fluid connection with the peripheral vein of the circulatorysystem of the patient via the IV tube.

In a fifth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, thetransducer comprises a pressure sensor disposed in fluid connection withan interior of the IV tube, and the physical phenomenon associated withthe PVP is a pressure within the interior of the IV tube.

In a sixth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theexecutable instructions further cause the evaluation unit to evaluationunit further determine whether the patient status metric indicates acondition of the patient is abnormal, and adjust operation of the pumpwhen the patient status metric indicates the condition of the patient isabnormal by changing a rate of flow of a fluid from the pump into thecirculatory system of the patient.

In a seventh aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theexecutable instructions that cause the evaluation unit to generate thefiltered time-domain PVP signal include instructions that cause theevaluation unit to remove the one or more active time segments from thetime-domain PVP signal.

In a eighth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theexecutable instructions further cause the evaluation unit to generatethe filtered time-domain PVP signal by, for each of one or more pairs ofthe active time segments, identifying one or more corresponding valueswithin both of the active time segments of the pair, and combining theactive time segments of the pair by aligning the one or morecorresponding values within both of the active time segments of thepair.

In a ninth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, theexecutable instructions that cause the evaluation unit to generate thefiltered time-domain PVP signal include instructions that cause theevaluation unit to estimate a third plurality of values as substitutevalues for the one or more active time segments, where the thirdplurality of values are estimated based upon the first plurality ofvalues without reference to the second plurality of values. Theexecutable instructions further cause the evaluation unit to generatethe filtered time-domain PVP signal by combining the first plurality ofvalues for the inactive time segments and the third plurality of valuesfor the active time segments.

In a tenth aspect of the present disclosure, which may be combined withany other aspect listed herein unless specified otherwise, the thirdplurality of values are estimated by performing at least one ofregression analysis, forward-backward slope calculation, two-sided slopedetection, and mirror matched filtering on at least the first pluralityof values.

In a eleventh aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theexecutable instructions that cause the evaluation unit to determine thepatient status metric include instructions that cause the evaluationunit to identify a plurality of frequencies associated with local maximaof the frequency-domain PVP signal, and determine the patient statusmetric based at least in part upon at least one of the plurality offrequencies associated with the local maxima.

In a twelfth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thepatient status metric is a blood volume metric indicating one or more ofthe following: hypovolemia, hypervolemia, or euvolemia.

In a thirteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, a devicefor monitoring a patient includes a peripheral venous pressure (PVP)sensor and an evaluation unit. The PVP sensor includes a transducerconfigured to monitor a physical phenomenon associated with a PVP withina peripheral vein of a circulatory system of the patient while thecirculatory system of the patient is connected to a pump. The evaluationunit includes a computer processor communicatively connected to the PVPsensor and a memory storing non-transitory executable instructions that,when executed by the computer processor, cause the evaluation unit toobtain a time-domain PVP signal comprising values of an electronicsignal associated with the PVP received from the transducer of the PVPsensor over a sample period. The sample period includes a plurality oftime segments, including (i) one or more active time segments duringwhich the pump is operating and (ii) one or more inactive time segmentsduring which the pump is not operating. The evaluation unit identifies afirst plurality of the values of the time-domain PVP signal associatedwith the one or more inactive time segments and a second plurality ofthe values of the time-domain PVP signal associated with the one or moreactive time segments, based upon evaluation of the values of thetime-domain PVP signal. The evaluation unit generates a filteredtime-domain PVP signal based upon the first plurality of the values andexcluding the second plurality of the values. The evaluation unitapplies a transformation to the filtered time-domain PVP signal togenerate a frequency-domain PVP signal. The evaluation unit determines apatient status metric for the patient based upon the frequency-domainPVP signal.

In a fourteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thetime-domain PVP signal comprises a first time series of discrete values,the filtered time-domain PVP signal comprises a second time series ofdiscrete values, and the second time series contains at least onesegment of a sequential plurality of values within the second timeseries that are equivalent to a corresponding segment of a sequentialplurality of corresponding values within the first time series.

In a fifteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theexecutable instructions that cause the evaluation unit to generate thefiltered time-domain PVP signal include instructions that cause theevaluation unit to remove the one or more active time segments from thetime-domain PVP signal.

In a sixteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, theexecutable instructions that cause the evaluation unit to generate thefiltered time-domain PVP signal include instructions that cause theevaluation unit to estimate a third plurality of values as substitutevalues for the one or more active time segments, where the thirdplurality of values are estimated based upon the first plurality ofvalues without reference to the second plurality of values, and generatethe filtered time-domain PVP signal by combining the first plurality ofvalues for the inactive time segments and the third plurality of valuesfor the active time segments.

In a seventeenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, a methodof monitoring a patient using a measurement associated with a peripheralvenous pressure (PVP) within a peripheral vein of a circulatory systemof the patient while the circulatory system of the patient is connectedto a pump includes monitoring, by a transducer, a physical phenomenonassociated with the PVP of the patient over a sample period, where thesample period includes a plurality of time segments, including (i) oneor more active time segments during which the pump is operating and (ii)one or more inactive time segments during which the pump is notoperating. The method includes obtaining, by a processor of anevaluation unit, a time-domain PVP signal comprising values of anelectronic signal associated with the PVP from the transducer based uponthe monitored physical phenomenon over the sample period. The methodincludes identifying, by the processor of the evaluation unit, a firstplurality of the values of the time-domain PVP signal associated withthe one or more inactive time segments and a second plurality of thevalues of the time-domain PVP signal associated with the one or moreactive time segments, based upon evaluation of the values of thetime-domain PVP signal. The method includes generating, by the processorof the evaluation unit, a filtered time-domain PVP signal based upon thefirst plurality of the values and excluding the second plurality of thevalues. The method includes applying, by the processor of the evaluationunit, a transformation to the filtered time-domain PVP signal togenerate a frequency-domain PVP signal. The method includes determining,by the processor of the evaluation unit, a patient status metric for thepatient based upon the frequency-domain PVP signal.

In a eighteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,generating the filtered time-domain PVP signal includes removing the oneor more active time segments from the time-domain PVP signal.

In a nineteenth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise,generating the filtered time-domain PVP signal includes estimating athird plurality of values as substitute values for the one or moreactive time segments, where the third plurality of values are estimatedbased upon the first plurality of values without reference to the secondplurality of values, and generating the filtered time-domain PVP signalby combining the first plurality of values for the inactive timesegments and the third plurality of values for the active time segments.

In a twentieth aspect of the present disclosure, which may be combinedwith any other aspect listed herein unless specified otherwise, thethird plurality of values are estimated by performing at least one ofregression analysis, forward-backward slope calculation, two-sided slopedetection, and mirror matched filtering on at least the first pluralityof values.

Additional features and advantages of the disclosed devices, systems,and methods are described in, and will be apparent from, the followingDetailed Description and the Figures. The features and advantagesdescribed herein are not all-inclusive and, in particular, manyadditional features and advantages will be apparent to one of ordinaryskill in the art in view of the figures and description. Also, anyparticular embodiment does not have to have all of the advantages listedherein. Moreover, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and not to limit the scope of the inventivesubject matter.

BRIEF DESCRIPTION OF THE FIGURES

Understanding that the figures depict only typical embodiments of theinvention and are not to be considered to be limiting the scope of thepresent disclosure, the present disclosure is described and explainedwith additional specificity and detail through the use of theaccompanying figures. The figures are listed below.

FIG. 1A illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system having a fluid source.

FIG. 1B illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system not having a fluid source.

FIG. 1C illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system including a sensor disposed within aperipheral vein.

FIG. 1D illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system including a pump.

FIG. 1E illustrates a block diagram of an exemplary PIVA system for usein measuring, analyzing, and responding to a patient's peripheral venousblood pressure, the system including an additional sensor for monitoringpatient position or movement.

FIG. 2A illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing afluid connection via a spur of an IV tube.

FIG. 2B illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing afluid connection via a capped IV tube.

FIG. 2C illustrates a block diagram of an exemplary PIVA device forimplementing some functions of the exemplary PIVA system, showing asensor disposed adjacent to an exterior wall of an IV tube.

FIG. 3 illustrates a flow diagram of an exemplary PIVA measurement andanalysis method for measuring and analyzing a patient's peripheralvenous blood pressure.

FIG. 4A illustrates an exemplary plot of time-domain representation of aPVP signal.

FIG. 4B illustrates an exemplary plot of frequency-domain representationof a PVP signal.

FIG. 5A illustrates an exemplary plot of time-domain representation of aPVP signal during operation of a noise-creating medical device.

FIG. 5B illustrates an exemplary plot of time-domain representation ofthe PVP signal after removing active time segments during which themedical device is operating.

FIG. 5C illustrates an exemplary plot of time-domain representation of afiltered PVP signal including estimates of values for the removed activetime segments.

FIG. 6 illustrates a flow diagram of an exemplary pressure signalfiltering method for removing noise artifacts related to operation of amedical device from a signal corresponding to a patient's peripheralvenous blood pressure.

FIG. 7 illustrates an exemplary PIVA comparison method for identifyingchanges in a patient status based upon comparison of PVP over time.

FIG. 8 illustrates a block diagram of exemplary processing performed byan exemplary PIVA module.

FIG. 9 illustrates a block diagram of an exemplary PIVA system,including a PIVA module.

FIG. 10 illustrates a block diagram of exemplary processing performed byan exemplary PIVA module.

FIG. 11 illustrates a flow diagram of an exemplary patient monitoringmethod using patient PVP.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment, asdescribing every possible embodiment would be impractical, if notimpossible. One of ordinary skill in the art could implement numerousalternate embodiments, which would still fall within the scope of theclaims. Unless a term is expressly defined herein using the sentence “Asused herein, the term ‘______’ is hereby defined to mean . . . ” or asimilar sentence, there is no intent to limit the meaning of that termbeyond its plain or ordinary meaning. To the extent that any term isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only, and it is not intended that suchclaim term be limited to that single meaning. Finally, unless a claimelement is defined by reciting the word “means” and a function withoutthe recital of any structure, it is not intended that the scope of anyclaim element be interpreted based on the application of 35 U.S.C. §112(f).

In many situations, it is important to monitor various informationassociated with a patient status or condition. The systems and methodsdisclosed herein improve upon existing techniques by using metrics orrepresentations of PVP measurements to generate patient status metrics.Such metrics or representations may be generated using frequency-domainPVP data derived from a time-domain PVP signal corresponding to the PVPmeasurements. Patient status metrics may be generated using a PIVA orother similar system to monitor and respond to changes in a patient'scondition, as discussed further herein. The systems, devices, andmethods disclosed below enable more efficient and more effectivemonitoring by using PVP measurements to determine the patient statusmetrics. This facilitates metric-based monitoring for a broader range ofpatient conditions that were previously susceptible to automaticmonitoring. This also facilitates monitoring of distinct types ofpatient conditions based upon measurements indicative of pressure in aperipheral vein, without needing specialized sensors to monitor eachtype of patient condition. Exemplary embodiments are described below.

PIVA System and Signal Noise

FIGS. 1A-E illustrate block diagrams of embodiments of an exemplary PIVAsystem 100 for use in measuring, analyzing, and responding to peripheralvenous blood pressure of a patient 102. The exemplary PIVA system 100 ora similar system may be used to implement the various techniques formonitoring patient status based upon measurements associated with PVPfor the patient 102. The PIVA system 100 may measure a pressure signalassociated with the patient's peripheral vein, analyze the pressureusing PIVA techniques to identify key frequency components of thepressure signal, and analyze the key frequency components of thepressure signal to determine patient status based upon one or moremetrics, as discussed below.

The exemplary PIVA system 100 illustrated in FIG. 1A includes an IV tube104 in fluid connection with the circulatory system of the patient 102.Specifically, a venous access device 106 may be inserted into aperipheral vein 108 of the patient 102 at an access point. The venousaccess device 106 may include a needle, catheter, cannula, or othermeans of establishing a fluid connection between the IV tube 104 and theperipheral vein 108. The venous access device 106 may be a separatecomponent connected to the IV tube 104 or may be formed as an integralportion of the of the IV tube 104. In either case, the venous accessdevice 106 may include a terminal end inserted into the peripheral vein108 at the access point and a connecting end that connects to a primaryportion of the IV tube 104. The primary portion of the IV tube 104 mayserve as a conduit between the venous access device 106 and a fluidsource 110.

At some point along the primary portion of the IV tube 104, a pressuresensor 112 may be disposed to monitor a physical phenomenon associatedwith PVP of the patient 102. In some embodiments, the pressure sensor112 may directly measure a pressure corresponding to the PVP, such as apressure in the interior of the IV tube 104. In such embodiments, ameasuring portion of a pressure transducer (e.g., a Piezoelectricpressure transducer) may be disposed in fluid connection with theinterior of the IV tube 104. The pressure sensor 112 may thus also be influid connection with the peripheral vein 108 of the patient through theIV tube 104 and the venous access device 106. The pressure sensor 112 isthereby enabled to measure pressure changes in the peripheral venoussystem of the patient 102 based upon changes in the fluid pressurewithin the IV tube 104. In other embodiments, the pressure sensor 112may indirectly measure a pressure corresponding to the PVP of thepatient 102 by measuring other phenomena, without being disposed influid connection with the interior of the IV tube 104. For example, thepressure sensor 112 may instead be attached to the exterior of the IVtube 104 and thereby disconnected from the interior of the IV tube 104or the fluid of the fluid source 110 (as illustrated in FIG. 2C). Thepressure sensor 112 may, in some such embodiments, measure pressurebased upon acoustic or optical phenomenon at the sensor location. Insome embodiments, the pressure sensor 112 may be disposed at aterminating end (i.e., a capped off end) of an IV tube 104 insertedspecifically for the purpose of measuring pressure within the peripheralvein 108, in a manner similar to that illustrated in FIG. 1B. In furtherembodiments, other sensors may be used instead of the pressure sensor112, such as sonic, electrical, temperature, or similar sensors tomeasure one or more of the following physical phenomena: pressure,sound, electrical resistivity or conductivity, electrical voltage orcurrent, light levels or properties (e.g., spectrum or frequencyshifts), or other similar phenomena. Whichever types of sensors areused, the sensors may be (but need not be) in fluid contact with theperipheral vein 108 of the patient through the IV tube 104 and thevenous access device 106 (or directly through the venous access device106) to measure the phenomena associated with the PVP of the patient102. In yet further embodiments, the sensor 112 may be disposed within aportion of a needle, catheter, or other venous access device 106 that isinserted within the peripheral vein 108 of the patient 106, asillustrated in FIG. 1C. Thus, the PVP may be measured in situ within theperipheral vein 108. Such in situ measurement is advantageous inasmuchas it obviates the effect of temperature, viscosity, and other factorson transmission of pressure within the IV tube 104.

In various embodiments, the pressure sensor 112 may be positioned atvarious distances from the access point of the peripheral vein 108, froma location within the peripheral vein 108 or a location proximate to theconnecting end of the venous access device 106 to a position proximateto the fluid source 110 or at a terminating end of the IV tube 104. Thepressure sensor 112 is illustrated in FIG. 1A as being at anintermediate location along the length of the IV tube 104 in order toillustrate better the various components of the PIVA system 100. In someembodiments, the pressure sensor 112 may directly measure fluid pressurewithin the IV tube 104. Specifically, the pressure sensor 112 mayinclude a transducer that provides an electronic pressure signalindicative of the pressure detected by the transducer to an analysiscomponent 114 via a connection 122. The electronic pressure signal maybe an analog electrical signal directly provided by the transducer ormay be a preprocessed digital signal indicating pressure values basedupon the transducer interface with the primary portion of the IV tube104. In embodiments in which the pressure sensor 112 is not in fluidconnection with the IV tube 104 or the peripheral vein 108, the pressuresensor 112 may nonetheless include one or more transducers to generateelectronic signals associated with the PVP. For example, the pressuresensor 112 may use one or more microphones disposed to detect sound atan exterior surface of an IV tube 104 to generate electronic pressuresignals indicative of pressure within the IV tube 104 as a proxy for PVPwithin the peripheral vein 108.

The analysis component 114 is communicatively connected to the pressuresensor 112 to receive the electronic pressure signal via the connection122. The analysis component 114 may include general-purpose orspecial-purpose processing hardware, such as microprocessors orspecial-purpose analysis circuits. As shown, the analysis component 114may include one or more units for performing the PIVA analysis. Aresponse unit 116 may identify and control responses based upon thepressure data from the pressure sensor 112. The response unit 116 maycontrol the presentation of alarms or may control the operation of thefluid source 110, such as by controlling the rate of fluid flow. Todetermine appropriate responses, the response unit 116 may receiveevaluation data from an evaluation unit 118, which may include metricsdetermined from the electronic pressure signal. The evaluation unit 118may obtain pressure values (or signal values directly or indirectlyassociated with PVP) from the electronic pressure signal and evaluatethe pressure values to determine information regarding the patient 102,such as blood volume metrics, position metrics, movement metrics, orother metrics as described in further detail below. The informationgenerated by the evaluation unit 118 may also be stored or presented forpatient monitoring. In alternative embodiments, additional, fewer, oralternative units may be included. For example, the evaluation unit 118may perform the functions ascribed to the response unit 116 herein.

The analysis component 114 may be communicatively connected to a monitor120 via a connection 126 in some embodiments. The monitor 120 may be aseparate monitor for displaying information regarding the patient or maybe incorporated into another device, such as a pump or other fluidsource device. The monitor 120 may also be communicatively connected tothe fluid source 110 via a connection 128 to receive and displayinformation associated with the fluid source 110. In some embodiments,the monitor 120 may be used to control the operation of the fluid source110, such as by adjusting fluid flow rate, duration of operation, modeof operation, or other similar control. The analysis component 114 maysimilarly be communicatively connected to the fluid source 110 viaconnection 124 in some embodiments. The analysis component 114 mayreceive information regarding operation of the fluid source 110 for usein evaluating the patient by the evaluation unit 118. The response unit116 may also communicate with the fluid source 110 to control operationof the fluid source 110 in response to information regarding the patientdetermined based upon the electronic pressure signal from the pressuresensor 112.

In some embodiments, the fluid source 110 may comprise a pump 111, asillustrated in FIG. 1D. Such pump may be disposed within the exemplaryPIVA system 100 to pump blood or other fluids into the peripheral vein108 of the patient 102. For example, the pump 111 may include an IVinfusion pump or a dialysis pump, such as a peristaltic pump. The pump111 may be configured to operate cyclically in a periodic or aperiodicmanner, having alternating intervals of operation (i.e., active timesegments) and rest (i.e., inactive time segments). By alternating thepump 111 between operating and rest intervals, periods of time in whichthe pump 111 is not operating may be used for PIVA analysis, asdescribed further below. In some embodiments, such as where the pump 111is a hemodialysis pump, the pump 111 may further be connected to thecirculatory system of the patient 102 by an additional IV tube 105(which may include or be further attached to an additional venous accessdevice 107), thereby creating an extracorporeal blood circuit throughthe pump 111 via the tubes 104 and 105. In such embodiments, the pump111 may draw blood out of the patient 102 through either of tubes 104 or105. The extracorporeal blood may then be processed according to atherapeutic regimen before being returned to the patient circulatorysystem (or may be replaced by another fluid that may be infused into thepatient circulatory system) through the other of the IV tubes 105 or104. Although described herein as one component, it should be understoodthat the pump 111 may comprise a plurality of pumping components (e.g.,a pair of pumps for extracting and returning blood or other fluids, ormultiple pumps in a common fluid system) in some embodiments.

In some embodiments, the exemplary PIVA system 100 may include one ormore additional sensors 150, as illustrated in FIG. 1E. The additionalsensors 150 may include pressure sensors, infrared sensors, opticalsensors, magnetic sensors, or the like. In various embodiments, eachadditional sensor 150 may be connected to the analysis component 114 viaa connection 152 or to the monitor 120 via connection 154, which may bewired or wireless connections. Such additional sensors 150 may bedisposed to monitor the presence, absence, location, or position of thepatient 102. For example, a pressure sensor may be disposed within ahospital bed to determine whether the patient 102 is within the bedbased upon a measurement of weight. Similarly, one or more sensors maybe disposed to determine whether such bed is flat or is partiallyelevated to facilitate a sitting posture. Other additional sensors 150may be disposed upon the patient 102 to monitor movement. For example, awristband sensor containing an accelerometer array may be worn by thepatient 102, which may measure data regarding at least some patientmovements. The additional sensors 150 may thus be disposed together withthe pressure sensor 112 within a PIVA device 130 or may be separatetherefrom. In further embodiments, the additional sensors 150 mayfurther include any of the following to measure orientation or motion ofthe patient: a real-time three-dimensional gyroscope, one or morecameras monitoring the local physical environment around the patient, ora microphone configured to monitor sounds in the local physicalenvironment. Sensor data from the additional sensors 150 may becorrelated with IV pressure measurements or other pressure-relatedmeasurements associated with the PVP of the patient.

The various connections 122, 124, 126, and 128 may each be wired orwireless connections in various embodiments. Moreover, some or all ofthe connections 122, 124, 126, and 128 may be internal to devices, suchas a PIVA device 130 or a PIVA-integrated fluid source 140.

The PIVA device 130 may incorporate the pressure sensor 112 and analysiscomponent 114 (along with associated connections) into a device that maybe attached to or within the IV tube 104 to perform PIVA monitoring ofthe patient 102. In some embodiments, the PIVA device 130 may furtherinclude one or more additional sensors 150 or other components describedherein. The PIVA-integrated fluid source 140 may include acomputer-controlled fluid reservoir or pump configured to utilize PIVAmonitoring of the patient 102 in controlling fluid flow. Like the PIVAdevice 130, the PIVA-integrated fluid source 140 may include thepressure sensor 112 and analysis component 114, along with the fluidsource 110 and the monitor 120 (along with associated connections).Alternative embodiments may include additional, fewer, or alternativecomponents in alternative configurations.

FIGS. 2A-C illustrate block diagrams of exemplary embodiments of a PIVAdevice 130 for implementing some functions of the exemplary PIVA system100. As illustrated in FIG. 2A, the exemplary PIVA device 130 may beconfigured to attach to a spur 104A of the IV tube 104, such as at onebranch of a Y-connector or a T-connector. Alternatively, the exemplaryPIVA device 130 may be configured to attach to a terminal end of the IVtube 104, as illustrated in FIG. 2B. In such embodiments, the PIVAdevice 130 may cap a terminating portion of the IV tube 104, such thatno fluid source 110 is connected to the peripheral vein 108 through thesame IV tube 104. Of course, a fluid source could be otherwise connectedto provide fluids to the patient 102 via another IV tube and anothervenous access device. In further embodiments, the PIVA device 130 may beconfigured to attach to the exterior of the IV tube 104, as illustratedin FIG. 2C. In such embodiments, one or more sensors of the PIVA device130 may monitor PVP without being in fluid connection with theperipheral vein 106 or the interior of the IV tube 104.

As discussed above, the PIVA device 130 may include a pressure sensor112 disposed such that a sensing portion is in contact with fluid in theIV tube 104, as illustrated in FIGS. 2A-B. In some embodiments, thepressure sensor 112 (or an alternative sensor) may instead be externalto the IV tube 104, as illustrated in FIG. 2C. However situated, thepressure sensor 112 is disposed to monitor a physical phenomenonassociated with pressure in the peripheral vein 108. Such physicalphenomenon may include pressure in the IV tube 104, expansion orcontraction of the IV tube 104, sound in the IV tube 104, vibrations ofthe IV tube 104, or other similar phenomena. The pressure sensor 112 maybe electrically communicatively connected to a microprocessor 132 via asystem bus 138. The microprocessor 132 (MP) may be furthercommunicatively connected to a program memory 134 and a communicationunit 136 (COMM UNIT) via the system bus 138. The program memory 134 maybe a non-transitory, non-volatile memory (e.g., a flash memory) storingexecutable instructions that may be executed by the microprocessor 132to evaluate the electronic pressure signal from the pressure sensor 112,determine patient information (e.g., blood volume metrics), determineappropriate responses to the determined patient information, and controlthe communication unit 136 to electronically communicate with the fluidsource 110 or monitor 120 via connections 124 or 126. The program memory134 may store a plurality of routines, scripts, or modules correspondingto units or sub-units of the analysis component 114, such as softwaremodules corresponding to response unit 116 or the evaluation unit 118.

The communication unit 136 may be a hardware component configured tosend and receive electronic data between the PIVA device 130 and thefluid source 110 or monitor 120 via connections 124 or 126. Theconnections 124 and 126 are illustrated as being wired connections inthe exemplary PIVA device 130, which may also be used to obtain powerfor the PIVA device 130. Alternatively, another power connection orbattery (not shown) may provide power to the PIVA device 130. Althoughshown as separate wired connections, the connections 124 and 126 may beseparate or combined wired or wireless connections. The connections 124and 126 may communicate with a communication component of the fluidsource 110 or monitor 120, which may include or be part of a pump 111.Such communications may include raw data generated by the pressuresensor 112, processed data related to measurements by the pressuresensor 112, data analyzed according to the methods described below, oralert signals or control commands determined based upon analyzed data.The fluid source 110 or monitor 120 may then take appropriate action orpresent appropriate information based upon the communications from theexemplary PIVA device 130.

FIG. 3 illustrates a flow diagram of an exemplary PIVA measurement andanalysis method 300 for measuring and analyzing a status of a patient102 based on PVP using the PIVA system 100. The method 300 may be usedto determine various patient status metrics, such as metrics related topatient blood pressure, blood volume, respiration, position or movement,or systemic vascular resistance. The method 300 may be performed by theevaluation unit 118 using an electronic pressure signal from thepressure sensor 112, the generation of which electronic pressure signalby the pressure sensor 112 may be included in the method 300 in someembodiments.

The method 300 begins with measuring a PVP data signal for the patient102 (block 302). The PVP data signal may be measured by using atransducer of the pressure sensor 112 to generate an electronic pressuresignal indicating PVP based upon a physical phenomenon associated withPVP. For example, this may be accomplished by measuring the pressurewithin the IV tube 104. Because the IV tube 104 is in fluid connectionwith the peripheral vein 108 of the patient 102 via the venous accessdevice 106, the pressure in the IV tube 104 measured by the pressuresensor 112 is associated with patient PVP (i.e., the pressure in theperipheral vein 108). In some embodiments of the PIVA system 100, thepressure within the IV tube 104 may be different from the PVP within theperipheral vein 108, but the pressure measured within the IV tube 104may nonetheless be proportional to the PVP in the peripheral vein 108.Thus, the measured PVP data signal may be adjusted to compensate fordifferences between the pressures, if desired. For example, adjustmentsmay be made based upon temperature, viscosity of the patient's blood ora fluid provided by the fluid source 110, or a gauge or rigidity of theIV tube 104. Whether adjusted or unadjusted, the PVP data signalmeasured by the pressure sensor 112 accurately represents changes inpressure over time, including both periodic pressure changes associatedwith respiratory and circulatory cycles and aperiodic pressure changesthat may be indicative of changes in patient condition. Similarly, a PVPdata signal generated by the pressure sensor 112 by components not influid contact with the interior of the IV tube 104 likewise provides arepresentation of the pressure within the peripheral vein 108 of thepatient 102. The PVP data signal may be the electronic pressure signalgenerated by the pressure sensor 112 or may be a data signal derivedtherefrom. In alternative embodiments, the PVP data signal may beevaluated in real-time as it is generated, or it may be stored for lateranalysis. Depending upon the components used to measure the PVP-relatedphenomenon, the PVP data signal may be generated or stored as an analog(i.e., as a continuous function or curve over a time segment) or adigital signal (i.e., as a set of discrete values representing distincttimes).

FIG. 4A illustrates an exemplary chart of a time-domain representationof the PVP data signal, which may be the electronic pressure signal fromthe pressure sensor 112. The chart illustrates a time-domain PVP signal402, which shows periodic increases and decreases in pressure associatedwith the patient heartbeat. Additionally, the time-domain PVP signal 402exhibits slower cyclical variation as a result of patient respiration.The chart also illustrates a respiration curve 404 that shows the effectof inspiration and expiration on the time-domain PVP signal 402. Becauseof the expansion of the lungs during inspiration, the measured pressurein the peripheral vein is higher during inspiration than duringexpiration, when the volume of the lungs is reduced. Other factorsinfluence PVP, such as blood volume and patient movement.

The time-domain PVP signal 402 is thus a combination of a plurality ofinfluences, both periodic (e.g., heart rate or respiration) andaperiodic (e.g., movement or blood loss). Because the resultingtime-domain PVP signal 402 will include noise from various sources, itmay be difficult to detect small changes in pressure that may serve asindications of patient status. Therefore, PIVA techniques utilize afrequency-domain evaluation of the PVP data signal in some embodiments,as described below. In other embodiments, time-domain or mixedtechniques may also be used to evaluate patient status or generatepatient status metrics. It should be recognized that, although thetime-domain representation of the PVP data signal is illustratedgraphically as a chart in FIG. 4A to illustrate the salient features ofthe data, it is not necessary to produce a chart or other graphicalrepresentation of such data signal. Instead, in some embodiments, thePVP data signal is processed by the evaluation unit 118 withoutgenerating a graphical representation of the time-domain PVP datasignal, or the graphical representation may be generated for user reviewseparately from evaluation.

Returning to FIG. 3, a plurality of data values may then be obtainedfrom the measured PVP data signal (block 304). The evaluation unit 118may sample values of the live or stored PVP data signal to obtain theplurality of data values. In some embodiments, the data values may besampled at fixed intervals over a period of time to obtain a pluralityof data values within an evaluation window, which may include storingthe plurality of data values associated with the window in temporary orpermanent electronic data storage. In further embodiments, data formultiple evaluation windows may be obtained, such that each evaluationwindow includes a plurality of data values. For example, concurrent timeperiods may be identified as separate evaluation windows, or evaluationwindows may be identified as time periods separated by an interveningperiod (e.g., twenty-second evaluation windows beginning every minute,thus separated by forty-second intervening periods). When the evaluationunit 118 samples values of a live (continuously updating) PVP datasignal, in some embodiments, the evaluation window may be updated on arolling basis to obtain new data values while covering time periods offixed duration. For example, the evaluation window may be repeatedlyupdated by adding new sample data values and removing the oldest sampledata values to maintain a window of a fixed duration (e.g., fiveseconds, ten seconds, twenty seconds, or some other time period) of themost recent PVP data from the pressure sensor 112. Where the evaluationunit 118 periodically obtains updates of new sample data values, thewindow may be updated (and the transformation and evaluation describedbelow may be performed for the updated window) every time a new datavalue is received. In an alternative embodiment, the plurality of datavalues may correspond to the continuous values of an analog PVP datasignal, which may be obtained and analyzed by analog electronicequipment (which may be part of the evaluation unit 118).

From the plurality of data values, the evaluation unit 118 generatesfrequency-domain data corresponding to the plurality of data values(block 306). Such frequency-domain data may be generated as a frequencydistribution representing the PVP data signal in the frequency domain asmagnitudes associated with each of a plurality of frequencies. This mayinclude applying a data transformation to the plurality of data valuesrepresenting a time-domain PVP signal to produce a frequency-domainrepresentation of the PVP signal. In a preferred embodiment, theevaluation unit 118 applies a fast Fourier transform (FFT) to thesampled plurality of data values to generate a frequency-domainrepresentation of the PVP signal. In a different embodiment, a differentdata transform (e.g., Laplace transform, Mellin transform, Hartleytransform, short-time Fourier transform, Chirplet transform, Hankeltransform, or any other continuous or discrete transform) may beimplemented to transform data to a frequency-domain representation ofthe PVP signal. The FFT may be applied periodically (e.g., every tenseconds, every minute, or every two seconds, with or without overlappingevaluation windows). In some embodiments, other analysis techniques thatcan identify local maxima according to frequency are contemplated, suchas wavelet transform, autocorrelation, or other signal analysistechniques that can segregate contributions to signal spectral energycontent over time-domain segments.

The frequency-domain data may include a plurality of values representingthe magnitude of various frequency components in the measured PVP datasignal based upon the plurality of data values. Such values may bediscrete or may be part of a curve of magnitudes corresponding tofrequencies, which curve may be generated by interpolation orapproximation between a finite number of values associated with a finitenumber of frequencies. Although FFT algorithms may be used to greateffect, other time-frequency transforms or other techniques of analyzingfrequency components of signals may be utilized to evaluate theplurality of data values. For example, in addition to other Fouriertransforms, the evaluation may include wavelet transforms ortime-frequency representations of the measured PVP data signal.

FIG. 4B illustrates an exemplary chart of a frequency-domainrepresentation of the PVP data signal, corresponding to the time-domainPVP signal 402 represented in the time domain in FIG. 4A. The chartillustrates the magnitude of each frequency component by a frequencycurve 406. As is customary, the horizontal axis represents frequency,and the vertical axis represents magnitude. Although the chart isexemplary, certain typical features may be discerned therein. Ofparticular interest are the several peaks (P_(N)) of the frequency curve406 associated with frequencies (F_(N)). Between the peaks, minorvariations in magnitude are seen, which may represent minor componentsof the time-domain PVP signal 402 associated with noise in the system orartifacts of the circulatory system of the patient 102 (e.g., movementsof the patient during measurement, or openings and closings of theatrioventricular and aortic valves) or in the exemplary PIVA system 100(e.g., pump noise).

Although the frequency-domain representation of the PVP data signal isillustrated in FIG. 4B as a chart to illustrate the salient features, itshould be understood that it is not necessary to produce a chart orother graphical representation of the frequency-domain data. Indeed, insome embodiments, no such graphical representation is generated.Instead, the frequency-domain data is processed by the evaluation unit118 as an intermediate process, the results of which are not directlypresented to a user of the system or device. In some embodiments, thefrequency-domain data may be stored in transitory or non-transitorymemory as values within a data list, data table, or similar datastructure.

Under ordinary conditions, the peak (P₀) with the lowest frequency (F₀)corresponds to the respiration rate of the patient 102, and the peak(P₁) with the next-lowest frequency (F₁) corresponds to the heart rateof the patient 102. One or more harmonic peaks (P_(H)) associated withharmonic frequencies (F_(H)) of the heart rate frequency (F₁) may beidentified in some embodiments. Such harmonic peaks (P_(H)) areassociated with local maxima of the frequency curve 406. The next twopeaks (P₂) and (P₃) of the frequency curve 406 are harmonic peaks(P_(H)) occurring at frequencies associated with the first and secondharmonics of the heart rate at the first harmonic frequency (F₂) and thesecond harmonic frequency (F₃). The harmonics occur at fixed multiplesof the heart rate frequency (F₁). Typically, these multiples aretypically integer multiples. Specifically, experimental data indicatethat first harmonic frequency (F₂) is approximately twice the heart ratefrequency (F₁), and the second harmonic frequency (F₃) is approximatelythrice the heart rate frequency (F₁).

Identification of the peaks (e.g., P₁, P₂, P₃) of the correspondingfrequencies (e.g., F₁, F₂, F₃), such as via the evaluation unit 118,provides for subsequent calculations of patient status (e.g.,hemodynamic status). For example, the peaks (e.g., P₁, P₂, P₃) of thecorresponding frequencies (e.g., F₁, F₂, F₃) may be used to calculate aPIVA Score, as further detailed herein.

Although not shown, additional peaks associated with third and higherharmonics of the heart rate may be identified in some embodiments. Thefurther harmonic frequencies (F₄, F₅, . . . F_(N)) typically occur atcorresponding sequential integer multiples of the heart rate frequency(F₁). For example, a second harmonic frequency may be represented by F₃,a third harmonic frequency may be represented by F₄, etc. Although somevariation exists in the observed frequencies of the peaks associatedwith the harmonic frequencies, the harmonic frequency peaks have beenfound to occur at frequencies that are typically within a range ofapproximately ten percent (i.e., ±10%) of the value of the heart ratefrequency above or below the integer multiples of the heart ratefrequency (F₁). The relationships between the magnitudes of the peaks(P_(N)) may vary, but the magnitude of the peak (P₁) associated with theheart rate frequency (F₁) should be greater than the magnitudes of thepeaks (P₂), (P₃), etc., associated with the harmonic frequencies (F₂),(F₃), etc., thereof.

Furthermore, it should be noted that while FIG. 4B illustrates thefrequency curve 406 as a number of parabolic peaks (e.g., P₀, P₁, P₂,P₃), other graphical representations of the frequency-domainrepresentation should be expected. For example, to the extent that thesystem is consistent (e.g., consistent patient respiration and heartrate), and the sampling rate is high enough (e.g., the sampling rate ofdata values measured in the time domain), the peaks (e.g., P₀, P₁, P₂,P₃) may be depicted graphically as vertical lines (e.g., parabolic peakswith unperceivable width or parabolic peaks with no width).

Although the present disclosure generally refers to the respiration rateas corresponding to the lowest-frequency peak (P₀), the heart rate ascorresponding to the next-lowest frequency peak (P₁), and so on, itshould be appreciated that any such reference is done for ease ofexplanation. To this end, in some embodiments, the time-domain PVPsignal may detect one or more frequencies lower than the respirationrate. For instance, gut frequencies tend to be associated with lowerfrequencies than a typical respiratory frequency. In these embodiments,the peak (P₀) with the lowest frequency (F₀) corresponds to a gutfrequency, and the peak (P₁) with the second-lowest frequency (F₁)corresponds to the respiratory frequency. Similarly, the heart ratefrequency and each of the corresponding harmonic frequencies wouldcorrespond to the next-lowest peak (P₂) and the following peaks (P₃, P₄,. . . P_(N)), respectively. It should be appreciated that in somefurther embodiments, the time-domain PVP signal may detect multiplefrequencies lower than the respiratory frequency. Accordingly, the peakindex corresponding to the respiration rate, the heart rate, and theheart rate harmonics may increase by the number of frequencies detectedlower than the respiration rate. As such, unless specifically describedotherwise, any reference to the respiratory rate corresponding to thelowest frequency peak (P₀) and the heart rate frequency corresponding tothe next-lowest frequency peak (P₁) is not limiting and also envisionsoffsetting the correspond peak indexes by the number oflower-than-respiration rate frequencies detected by the time-domain PVPsignal.

Turning again to FIG. 3, the evaluation unit 118 further identifies aplurality of frequencies (F_(N)) corresponding to peaks (P_(N)) of thefrequency-domain representation of the PVP signal (block 308), such asthe frequency curve 406. The evaluation unit 118 may first identifyvalues indicating peaks (P_(N)) in the frequency-domain representationof the PVP signal by comparison of the frequency-domain PVP signalvalues, then identify the corresponding frequencies (F_(N)) associatedwith the identified peak values (P_(N)). To determine the peak values(P_(N)), the evaluation unit 118 may utilize any of various methods toidentify local maxima as peaks, including methods based upon any or allof a comparison of the relative magnitudes of local maxima,establishment of fixed or dynamic frequency bands around each peak, orcomparison of full width at half maximum for local maxima. For example,a band-pass filter may be employed to separate segments of thefrequency-domain representation of the PVP signal to further identifylocal maxima. This may be particularly useful in identifying harmonicpeaks (P_(N)) and corresponding harmonic frequencies (F_(H)) becausesuch harmonics occur at integer multiples of the heart rate frequency(F₁).

As an example, a band-pass filter centered around a frequency twice theheart rate frequency (F₁) and having a band width of twenty percent ofthe heart rate frequency (F₁) may be used to define a range of thefrequency-domain representation of the PVP signal that contains thefirst harmonic peak (P₂). The first harmonic frequency (F₂) may then beidentified by simply determining the frequency associated with the localmaximum value of the frequency-domain representation of the PVP signalwithin such range. By employing these or other known techniques, thepeaks (P_(N)) of the frequency-domain representation of the PVP signalmay be distinguished from other local maxima arising from noise or otherminor phenomena in the circulatory system.

Once the plurality of frequencies (F_(N)) associated with the peaks(P_(N)) have been identified, the evaluation unit 118 may analyze themagnitudes of the frequency-domain representation of the PVP signal atone or more of the frequencies (F_(N)) to determine one or more aspectsof patient status (block 310). Such analysis may include determining oneor more patient status metrics, such as a blood volume metric,respiratory volume metric, patient position metric, patient movementmetric, systemic vascular resistance metric, other metric relating tothe systemic vascular resistance (e.g., mean arterial pressure, meanvenous pressure, cardiac output), or the like for the patient 102. Forexample, the patient status metrics may include a blood volume metricindicating one of the following hemodynamic states of the patient 102:hypovolemia, hypervolemia, or euvolemia. Hemodynamic states of thepatient 102 may be determined as a score or as a category of patientstatus in various embodiments. In further embodiments, time-domainanalysis may additionally or alternatively be performed to evaluate thePVP signal, as discussed elsewhere herein.

Some patient status metrics may be determined directly from the one ormore frequencies (F_(N)) or magnitudes of the frequency-domainrepresentation of the PVP signal associated therewith. For example,respiratory depth may be determined based upon the magnitude associatedwith the respiratory frequency (F₀) (i.e., the magnitude of therespiratory peak (P₀)), or a blood volume metric may be determined basedupon the magnitude associated with the heart rate frequency (F₁) (i.e.,the magnitude of the heart rate peak (P₁)). As another example, a bloodvolume metric indicative of patient hemodynamic state (e.g., hypovolemiaor hypervolemia) may be directly measured or calculated.

For example, as previously mentioned, subsequent to performingtransformation, the evaluation unit 118 may identify the peaks (e.g.,P₁, P₂, P₃) of the corresponding frequencies (e.g., F₁, F₂, F₃). Theseindividual peaks (e.g., P₁, P₂, P₃) corresponding to the variousfrequencies, such as the heart rate frequency F₁, the first harmonic ofthe heart rate frequency F₂, and the second harmonic of the heart ratefrequency F₃ may then be used in an equation to calculate a PIVA Score.PIVA Score, representative of a patient's fluid status, is also acorollary for pulmonary capillary wedge pressure. Because pulmonarycapillary wedge pressure is an indicator of fluid status (e.g.,hypervolemia or hypovolemia), the PIVA Score, likewise, isrepresentative of a patient's fluid status.

In an embodiment, the equation to calculate PIVA Score is represented bythe following:

${{PIVA}\mspace{14mu} {Score}} = {{c_{3}{\tanh \left( \frac{g_{0} + {g_{1}{mag}^{f\; 1}} + {g_{2}{mag}^{f\; 2}} + {g_{3}{mag}^{f\; 3}}}{2} \right)}} + {c_{2}{\tanh \left( \frac{h_{0} + {h_{1}{mag}^{f\; 1}} + {h_{2}{mag}^{f\; 2}} + {h_{3}{mag}^{f\; 3}}}{2} \right)}} + {c_{1}{\tanh \left( \frac{i_{0} + {i_{1}{mag}^{f\; 1}} + {i_{2}{mag}^{f\; 2}} + {i_{3}{mag}^{f\; 3}}}{2} \right)}} + c_{0}}$

Each of c₀, c₁, c₂, c₃, g₀, g₁, g₂, g₃, h₀, h₁, h₂, h₃, i₀, i₁, i₂, andi₃ are constants. Each of mag^(f1), mag^(f2), and mag^(f3) representsthe individual magnitudes of each of the respective frequencies (e.g.,F₁, F₂, F₃). These magnitudes are also commonly referred to herein aspeaks of frequencies. For example, mag^(f1) may also be referred to aspeak P₁ herein, associated with heart rate frequency F₁. Similarly, forexample, mag^(f2) may also be referred to as peak P₂ herein, associatedwith first harmonic frequency F₂. Similarly, for example, mag^(f3) mayalso be referred to as peak P₃ herein, associated with second harmonicfrequency F₃. For example, and with reference to FIG. 4B, P₁, referredto in the PIVA Equation as mag^(f1), is the magnitude of the heart ratefrequency (F₁), P₂, referred to in the PIVA Equation as mag^(f2), is themagnitude of a first harmonic frequency (F₂), and P₃, referred to in thePIVA Equation as mag^(f3), is the magnitude of a first harmonicfrequency (F₃).

Evaluation unit 118 calculates the PIVA Score, which is unitless. In arelated embodiment, PIVA system 100 displays the PIVA Score (e.g., viamonitor 120). By calculating PIVA Score, a patient's fluid status may bereadily determined (e.g., hypovolemia, hypervolemia, or euvolemia).Preferably, the calculated PIVA Score has an agreement with pulmonarycapillary wedge pressure of ±8 mmHg with limits of agreement of 95%confidence interval.

In an embodiment, additional peak magnitudes corresponding to variousfrequencies (e.g., P₄ corresponding to F₄, a third harmonic frequency)may also be used in calculating the PIVA Score (e.g., implementingadditional constants as well) for greater accuracy in calculation.

In an embodiment, the calculation or measurement can be directly relatedto a magnitude or change in magnitude of a harmonic peak (P_(H))associated with a harmonic frequency (F_(H)), such as a change from amagnitude at a prior time when the patient's hemodynamic state was known(e.g., a baseline measurement prior to surgery). As yet another example,heart rate variability may be determined based upon changes in the heartrate frequency (F₁) over time or by measuring the width of a portion ofthe frequency-domain representation of the PVP signal associated withthe heart rate peak (P₁) (e.g., the full width at half maximum).

In some embodiments, the patient status metric may be determined basedupon a comparison of magnitudes associated with different frequencypeaks (F_(N) and F_(M)) based upon the same plurality of data values(i.e., for the same evaluation window). For example, a ratio of themagnitudes associated with heart rate and first harmonic frequencies F₁and F₂ may be used to determine a hemodynamic metric for the patient,such as a systemic vascular resistance or a blood volume score. Suchratios may be particularly useful in normalizing magnitudes associatedwith harmonic frequencies (F_(H)) to obtain more robust and moreaccurate patient status metrics. Similarly, ratios between magnitudes ofthe frequency-domain representation of the PVP signal associated withdifferent harmonic frequencies (e.g., F₂ and F₃) may be used todetermine hemodynamic state of the patient 102 (e.g., blood volume). Infurther embodiments, the patient status metric may be determined basedupon a comparison of magnitudes associated with the same one or morefrequencies (F_(N)) of peaks (P_(N)) determined for differentpluralities of data values (i.e., for different evaluation windows). Forexample, analysis of a change in the absolute or relative magnitudeassociated with heart rate frequency F₁ over time may be used todetermine a hemodynamic metric. Information regarding the patient statusmay be stored in a memory, presented to a user via the monitor 120, orused by the response unit 116 to generate and implement a response(e.g., presenting an alarm or controlling the operation of the fluidsource 110), including any of the responses discussed further below.

In further embodiments, additional information regarding the patient maybe used in determining some patient status metrics, or such additionalinformation may be monitored for use with the patient status metrics.For example, information regarding a patient position or movement (e.g.,a patient movement metric) may be separately monitored to providecontext for the patient status metric or to supplement the patientstatus metric. To this end, additional patient metrics may be separatelymonitored by additional sensors 150 collecting data regarding positionsor movements of the patient 102, or multiple patient metrics may bedetermined by analysis of the PVP signal monitored via the pressuresensor 112. For example, a sudden shift in a patient metric derived fromPVP measured by the pressure sensor 112 (such as heart rate frequency F₁or associated magnitude P₁) and a spike in measured acceleration from anaddition sensor 150 may be combined to determine the patient has likelyfallen. As another example, a frequency-domain analysis of the PVPsignal may and a time-domain analysis of the PVP signal (e.g., waveformanalysis or pattern detection) may both be performed to generate patientmetrics, which may then be combined or analyzed together to evaluate thepatient status. The additional patient metrics may be evaluated toverify appropriateness of responses to changes in a patient statusmetric. Thus, if a patient status metric indicates a possibility of atransient condition at the same time as an additional patient metricindicates a patient movement, the patient status metric may bedetermined to be the result of the patient movement, so no response maybe required. Alternatively, if the additional patient metric confirms apatient status metric that indicates a patient movement or exertion fora patient who requires ambulatory assistance, an alert may be generatedto warn responsible personnel that that patient may be attempting towalk without assistance. In some embodiments, the additional informationmay include information indicating a patient condition or limitations,such as patient condition information entered by a physician or nurse.

FIGS. 5A-C illustrate exemplary charts of time-domain representations ofa PVP signal that includes noise artifacts, such as from operation of apump 111 or other fluid source 110. The exemplary charts illustratevarious stages or types of processing that may be performed by theanalysis component 114. FIG. 5A illustrates a PVP data signal 502 thatincludes both inactive segments 502I associated with inactive timesegments during which the pump 111 is not operating and active segments502A associated with active time segments during which the pump 111 isoperating. To show the effect of pump activity on the PVP data signal502, FIG. 5A further illustrates operation of the pump 111 by chartingthe pump control signal 504 on the same time scale. The pump controlsignal 504 is illustrated for simplicity as a binary signal, with avalue of “1” indicating active pumping and a signal of “0” indicatinginactivity. In alternative embodiments, however, alternative types ofpump control signals may be used to control the power or mode ofoperation of the pump 111.

As illustrated in FIG. 5A, the pump 111 is not operating during thefirst inactive time segment between times t₀ and t₁, so the values ofthe PVP signal 502 during this time segment form an inactive-pump PVPsignal 502I. The inactive-pump PVP signal 502I represents PVPmeasurements corresponding to pressure in the circulatory system of thepatient 102 without interference from the pump 111. As such, theinactive-pump PVP signal 502I is similar to the time-domain PVP signal402, described above. Thus, the values of the inactive-pump PVP signal502I may be used to perform further analysis according to PIVA or otherfrequency-domain methods, as discussed herein. As further illustrated,the pump 111 is operating during the first active time segment betweentimes t₁ and t₂, immediately following the first inactive time segment.The values of the PVP signal 502 during the first active time periodform an active-pump PVP signal 502A, the values of the which includenoise artifacts from the operation of the pump 111. The noise artifactsof such active time segments inhibit PIVA and other related analysis, soit is useful to remove, replace, or adjust the active-pump PVP signal502A prior to further analysis. Additional second and third inactivetime segments associated with inactive-pump PVP signals 502I duringwhich the pump 111 is not operating are further illustrated betweentimes t₂ and t₃ and between times t₄ and t₅. An additional active timesegment associated with active-pump PVP signals 502A during which thepump 111 is operating is illustrated between times t₃ and t₄. Althoughthe active time segments and inactive time segments are illustrated asadjacent in time in the exemplary chart, some embodiments may includetransition periods that are neither part of any inactive time period norpart of any active time period.

FIG. 5B illustrates an exemplary cleaned PVP signal 508 comprising onlythe inactive-pump PVP signals 502I. The exemplary cleaned PVP signal 508may be generated by simply removing data values associated with theactive time segments, leaving gaps 506 in the cleaned PVP signal 508. Inorder to remove the active time segments, the analysis component 114 mayfirst identify one or more of either or both of active time segments orinactive time segments. In some embodiments, information from the pump111 (such as the pump control signal 504) may be used to identify activetime segments or inactive time segments. In preferred embodiments,however, the analysis component 114 may identify the active timesegments or inactive time segments based upon the values of the PVPsignal 502. The analysis component 114 may identify the active timesegments or inactive time segments based upon magnitudes of the valuesor changes in the values of the PVP signal 502, as discussed furtherbelow.

Once generated, the cleaned PVP signal 508 may be directly analyzedaccording to the methods described herein, or the PVP signal 508 may befurther adjusted further prior to transformation to the frequencydomain. For example, the cleaned PVP signal 508 may be adjusted toremove the gaps 506 by aligning the inactive-pump PVP signals 502I to bepartially overlapping based upon a periodicity of the inactive-pump PVPsignals 502I. As another example, the cleaned PVP signal 508 may beadjusted to fill the gaps 506 with estimated values based upon theinactive-pump PVP signals 502I, as illustrated in FIG. 5C.Alternatively, instead of estimating the gaps 506, the inactive-pump PVPsignals 502I can be connected via other means, such as via straightlines connecting an end point of one inactive pump PVP signal 502I to astart point of a second inactive pump PVP signal 502I (e.g., a straightline across gap 506). Although the inactive-pump PVP signal 502Iassociated with a single inactive time segment may be sufficient forfrequency-domain analysis of patient status metrics if the inactive timesegment is of sufficiently long duration, the inactive time segments maybe of too short durations to allow accurate analysis. In such instances,combining a plurality of inactive-pump PVP signals 502I over acorresponding plurality of inactive time segments facilitates furtheranalysis by providing more data for evaluation. Even when individualinactive time segments are sufficiently long to allow frequencyanalysis, the accuracy may be improved by adding additional data valuesassociated with additional inactive time segments.

FIG. 5C illustrates an exemplary adjusted PVP signal 510 comprising theinactive-pump PVP signals 502I and estimated PVP signals 502E to fillthe gaps 506. The values of the estimated PVP signals 502E may beestimated based upon the values of the inactive-pump PVP signals 502I ofthe cleaned PVP signal 508, as discussed further below. By filling thegaps 506 with the estimated PVP signals 502E, the resulting adjusted PVPsignal 510 may be better suited for some types of further analysis.Specifically, the adjusted PVP signal 510 represents a comprehensivetime series of data without noise artifacts from operation of the pump111, which may be analyzed without further adjustment for the effects ofpumping. It should be noted that the adjusted PVP signal 510 may beobtained from the measured PVP signal 502 alone, without reference toextrinsic data regarding the pump 111. Thus, extrinsic data regardingtimes of pump operation (e.g., time periods of pump operation) orcharacteristics of pump operation (e.g., pump speed, pump volume, ormodels of noise artifacts generated by the pump) are not needed in orderto generate the adjusted PVP signal 510.

Although FIG. 5C illustrates the estimated PVP signals 502E as onlyfilling the gaps 506 created by removing the active-pump PVP signals502A, some embodiments may include estimating the entire adjusted PVPsignal 510. In such embodiments, both the active-pump PVP signals 502Aand the inactive-pump PVP signals 502I may be replaced with estimatedPVP signals 502E to generate the adjusted PVP signal 510. Although suchapproach may reduce accuracy of the analysis in some respects byreplacing measured values of the inactive-pump PVP signals 502I withestimated values of the estimated PVP signals 502E, the approach maybetter facilitate further analysis by eliminating discontinuities at theboundaries between the active and inactive time segments (i.e., at timest₁, t₂, t₃, and t₄). In yet further embodiments, discontinuities may beaddressed by adjusting values of one or more of the inactive-pump PVPsignals 502I or the estimated PVP signals 502E that occur near theboundaries between the active and inactive time segments to smooth thetransitions. In any case, the active-pump PVP signals 502A are excludedfrom the adjusted PVP signal 510 and are replaced with the estimated PVPsignals 502E.

FIG. 6 illustrates a flow diagram of an exemplary pressure signalfiltering method 600 for removing noise artifacts related to operationof a medical device from a signal corresponding to the PVP of thepatient 102. The filtering method 600 may be implemented by theevaluation unit 118 to obtain, filter, and analyze a PVP signal todetermine a patient status metric. Noise artifacts from the operation ofa pump 111, other fluid source 110, or similar medical device canobscure ordinary PVP measurements during operation. For analyticalmethods such as PIVA, these noise artifacts must be removed or otherwiseaddressed prior to further processing in order to obtain accuratemetrics. In contrast with other methods of addressing device noiseartifacts, the filtering method 600 identifies and removes signal valuesassociated with active time segments from a PVP signal containing activetime segments of device operation and inactive time periods when thedevice is inactive. To do this, a time-domain PVP signal (such as PVPsignal 502) is obtained and processed to remove signal values associatedwith active time segments (such as active-pump PVP signals 502A) togenerate a filtered time-domain PVP signal (such as cleaned PVP signal508 or adjusted PVP signal 510). The filtered time-domain PVP signal maythen be transformed to the frequency domain and analyzed according tothe methods discussed herein to determine one or more patient statusmetrics.

The filtering method 600 begins by obtaining a time-domain PVP signalfrom measurements associated with pressure in a peripheral vein of thepatient 102 (block 602). The time-domain PVP signal may be directlygenerated by the pressure sensor 112 or may be derived from sensormeasurements, as discussed elsewhere herein. As also described elsewhereherein, the time-domain PVP signal may be obtained by monitoring thepressure sensor 112 or by accessing a stored PVP data signal. In someembodiments, the evaluation unit 118 may monitor and record data from atransducer to generate the time-domain PVP signal. The time-domain PVPsignal may include one or more of each of the following: (i) active timesegments during which the pump 111 is operating (i.e., actively pumping)and (ii) inactive time segments during which the pump 111 is notoperating (i.e., not actively pumping). The active time segments and theinactive time segments may alternate periodically or aperiodically.Although the pump 111 may be configured to operate in such a manner asto inherently produce both active and inactive time segments during theordinary course of use, the active time segments are periods duringwhich the pump 111 is generating noise artifacts by active operation,while the inactive time segments are periods during which the pump 111is not generating significant noise artifacts by passive or inactiveoperation (e.g., a rest period between cyclical pumping). To enablefurther analysis of the measured PVP, the evaluation unit 118 mayidentify and filter the active and inactive time segments.

The filtering method 600 may, therefore, identify values of thetime-domain PVP signal associated with the active time segments or theinactive time segments (block 604). The evaluation unit 118 mayautomatically identify the active time segments, the inactive timesegments, or both the active and inactive time segments based upon thevalues of the time-domain PVP signal. In preferred embodiments, theevaluation unit 111 may identify the time segments based solely uponanalysis of the time-domain PVP signal, without reference to additionalextrinsic information regarding the characteristics or operating statusof the pump 111 that is not contained in or derived from the time-domainPVP signal (e.g., previously determined pump operating parameters or acontrol signal controlling operation of the pump). Thus, the evaluationunit 118 may identify the time segments in the same manner, regardlessof the characteristics, configuration, or settings of the pump 111, andwithout requiring adjustments to or further configuration of theevaluation unit 118. In various embodiments, the evaluation unit 118 mayautomatically identify the time segments based upon magnitudes of thevalues of the time-domain PVP signal or based upon changes in magnitudesof the values of the time-domain PVP signal. The values may be analyzedindividually or in sets containing a plurality of values, according toone or more set metrics applied to the sets.

For individual values of the time-domain PVP signal, each of a pluralityof values may be compared to one or more threshold levels to determinewhether the value is associated with a time within an active timesegment or an inactive time segment. For example, values above an upperthreshold level may be identified as being associated with an activetime segment, or values below a lower threshold level may be identifiedas being associated with an inactive time segment. The values may begrouped based upon such comparisons to identify the active and inactivetime segments. While the upper and lower threshold levels may beidentical in some embodiments, they may be distinct levels in otherembodiments. When distinct, an indeterminate range exists in whichvalues cannot be assigned to either active time segments or inactivetime segments. Such indeterminate values may be further analyzed basedupon the surrounding time segments to determine whether suchindeterminate values belong to active time segments, inactive timesegments, or transition time segments. In some embodiments, outliervalues may be discarded or identified as being part of an active orinactive time segment based upon values surrounding such outlier valuesin time (i.e., preceding and following the outlier value). In order toremove the noise artifacts more completely, transition time segments maybe treated as active time segments in some embodiment for the purpose ofgenerating the filtered time-domain PVP signal.

For sets of values of the time-domain PVP signal, each set may beanalyzed using one or more set-metrics to determine whether the set isassociated with an active or inactive time segment. In preferredembodiments, each set contains values of the time-domain PVP signal thatare adjacent in time, forming a time series of values of the PVP signal.Thus, each set is associated with a set-specific time period andcomprises values associated with times within the set-specific timeperiod. The set-specific time periods of the sets may cover fixeddurations or may be of variable durations, and the set-specific timeperiods may be overlapping or non-overlapping. The sets may includesampled values from the time-domain PVP signal, or the sets may includeall values of the time-domain PVP signal associated with times withinthe set-specific time periods of the corresponding sets. In particularlypreferred embodiments, the set-specific time periods can benon-overlapping but adjacent sets covering all time periods within ananalysis time period for which data is available for an uninterruptedduration of the time-domain PVP signal, such that each value of thetime-domain PVP signal during the analysis time period is in exactly oneof the sets. Thus, the active or inactive time segments may beidentified as collections of one or more sets by identifying the sets asbeing associated with either active or inactive time segments.

To identify a set as being associated with an active or inactive timesegment, the values of the time-domain PVP signal within the set may beevaluated using one or more set-metrics. The set-metrics may includefunctions that determine an average value, a maximum value, a minimumvalue, a distance between maximum and minimum values, an average changebetween values (or absolute value thereof), a variance of the set, oranother metric of the values in the set. Once a set-metric has beendetermined by evaluating the values of a set, the set-metric may becompared against a set-threshold level associated with the set-metric toidentify the set as being associated with an active time segment or aninactive time segment. For example, sets may be identified as beingassociated with active time segments when a set-metric is above theset-threshold level for the set-metric or being associated with inactivetime segments when a set-metric is below the set-threshold level for theset-metric.

In some embodiments, the set-metric may determine changes between valueswithin a set, such as a rate of change. Such rate of change may be anaverage rate of change, a maximum rate of change, or other measures ofchanges between the values. Set-metrics regarding changes or rates ofchange between values may be used to determine beginning times or endingtimes of active or inactive time segments by comparison against athreshold associated with the starting or stopping of active pumping.PVP may spike when the pump 111 begins active pumping at the start of anactive time segment and drop precipitously when the pump 111 stopsactive pumping at the end of the active time segment. Thus, large andrapid changes in the values of the time-domain PVP signal may be used toidentify beginnings or ends of the active and inactive time segments.For example, the beginning time of an active time segment may beidentified by determining a change or rate of change set-metric is abovea pumping start threshold, and the beginning time of an inactive timesegment may be identified by determining a change or rate of changeset-metric is below a pumping stop threshold. Active and inactive timesegments may then be identified based upon such beginning or endingtimes.

Once the active and inactive time segments are identified in thetime-domain PVP signal, the evaluation unit 118 may generate a filteredtime-domain PVP signal (block 606). The filtered time-domain PVP signalmay be an adjusted PVP signal 510 having estimated PVP signals 502E (asillustrated in FIG. 5C) or may instead be a cleaned PVP signal 508 thatsimply removes the active-pump PVP signals 502A (as illustrated in FIG.5B). The filtered time-domain PVP signal is generated based upon thetime-domain PVP signal and excludes the values of the time-domain PVPsignal associated with the active time segments. In contrast to othermethods that attempt to correct for pump noise artifacts by estimatingand removing the noise artifacts themselves, the filtering method 600estimates what the PVP signal would have been if the pump 111 had notbeen operating.

As illustrated above in the cleaned time-domain PVP signal 508, thefiltered time-domain PVP signal may be generated by removing the valuesassociated with one or more identified active time periods from thetime-domain PVP signal. Where the time-domain PVP signal comprises asequential time series of discrete values, the filtered time-domain PVPsignal may be generated by removing those values identified by theircorresponding times as falling within an active time segment, therebyleaving one or more sequential time series of discrete valuescorresponding to times falling within inactive time segments. In someembodiments, the filtered time-domain PVP signal may further be adjustedor normalized before further analysis. For example, the remaining valuesassociated with inactive time segments may be stitched together to avoidhaving gaps in the filtered time-domain PVP signal (such as the gaps506). To do this, corresponding values within each of a plurality ofinactive time segments may be identified, and the inactive time segmentsmay be combined by aligning these identified corresponding values. Thus,the beginning of one inactive time segment may be aligned with the endof the preceding inactive time segment such that the cycles (i.e., thecardiac cycles of the patient) are aligned. This may further requireremoving or blending overlapping values of one or both of the inactivetime segments to produce an uninterrupted filtered time-domain PVPsignal.

As illustrated above in the adjusted PVP signal 510, the filteredtime-domain PVP signal may alternatively be generated by replacing thevalues associated with the one or more identified active time periodswith substitute values. The substitute values are determined based uponthe values associated with one or more inactive time segments in thetime-domain PVP signal. Thus, the filtered time-domain PVP signal may begenerated by combining the values of the time-domain PVP signalassociated with the inactive time segments with the substitute valuesfor the active time segments to produce an uninterrupted signal or timesequence of values. In some embodiments, the substitute values may begenerated by estimating values for the active time segments based upon amodel determined by regression analysis, principal component analysis,or similar techniques. The model parameters may be estimated by ordinaryleast squares regression on the values associated with the inactive timesegments. In preferred embodiments, however, the model may be estimatedby least cubes regression on the values associated with the inactivetime segments, which produces improved results for PVP signals undermany circumstances. In some embodiments, the substitute values may beadjusted near the boundaries between the active and inactive timesegments in order to smooth the transitions between the inactive timesegment values and the substitute values. In further embodiments, thesubstitute values may be estimated for both the active and inactive timesegments, in which case the values of both may be replaced by theestimated substitute values to generate the filtered time-domain PVPsignal. Such filtered time-domain PVP signals may be beneficial in someinstances, inasmuch as such signals avoid breaks or discontinuities ofthe signal at boundaries between the active and inactive time segments.

Once the filtered time-domain PVP signal has been generated for one ormore time periods (such as the evaluation windows discussed above), theevaluation unit 118 may further analyze the data by generatingfrequency-domain PVP data from the one or more filtered time-domain PVPsignals (block 608). In a manner similar to that discussed elsewhereherein, a time-frequency transform (such as FFT) may be applied to thefiltered time-domain PVP signals to generate frequency-domain PVP dataas a representation of PVP in the frequency domain after filtering toremove the noise artifacts from operation of the pump 111. Suchfrequency-domain PVP data may be generated as frequency distributionsassociated with the one or more filtered time-domain PVP signals. Byusing the filtered time-domain PVP signal to generate thefrequency-domain PVP data, PVP can be analyzed for patients connected tocyclically operating pumps 111, despite the noise artifacts generated bypump operation. If the pump 111 is directly connected to the patient'scirculatory system, the methods described herein enable analysis atrates of operation up to the point at which the inactive time segmentsbecome too short and too infrequent for reliable filtering (e.g.,approximately 250 cc/minute for most adult patients with typical heartrate and respiratory rate using an infusion pump such as the SIGMASpectrum® infusion system produced by Baxter International Inc.). In anembodiment, the evaluation unit 118 further normalizes thefrequency-domain PVP data. For example, the evaluation unit 118 maynormalize frequency-domain PVP data to take into account inactive timesegments. The frequency-domain data may then be further analyzed todetermine one or more patient status metrics (block 610). Suchfrequency-domain analysis may include analysis of the frequencies ormagnitudes of frequency peaks (F_(N)), as discussed in further detailelsewhere herein. In some embodiments, this may include comparingfrequency-domain PVP data to determine changes in patient statusmetrics.

Because comparisons of changes in frequencies and associated magnitudesare particularly useful for monitoring patient condition via patientstatus metrics, a discussion of such comparisons is next described.Similar methods of comparison of metrics of the PVP signal in thetime-domain across multiple time periods may likewise be performed tomonitor patient condition in further embodiments. FIG. 7 illustrates anexemplary PIVA comparison method 700 for identifying changes in apatient status based upon comparison of frequency-domain representationsof PVP signals associated with different times. The PIVA comparisonmethod 700 may be implemented by the evaluation unit 118 and theresponse unit 116 to determine and respond to changes in patient statusbetween time periods. For example, the evaluation unit 118 may determineand compare frequency-domain representations of PVP based uponelectronic pressure signals received during multiple time periods todetermine changes in patient metrics, such as blood pressure, bloodvolume, respiration, position or movement, or systemic vascularresistance. Specifically, the evaluation unit 118 may compare relativeor absolute magnitudes associated with frequencies (F_(N)) of peaks(P_(N)) in the frequency distributions determined for each time periodto identify changes in patient status that may be used by the responseunit 118 to determine and implement response actions.

The exemplary method 700 begins by obtaining a first frequencydistribution associated with a first time period (block 702) and asecond frequency distribution associated with a second time period(block 704). Each of the first and second frequency distributions may begenerated as the frequency-domain data corresponding to the plurality ofdata values from the PVP data signal by the method 300 or the filteringmethod 600, as described above. The first and second time periods maycorrespond to first and second evaluation windows, as discussed above,each evaluation window being associated with a plurality of data valuessampled or received by the evaluation unit 118. The data values for eachof the first and second evaluation windows may be stored in a volatileor non-volatile memory until needed by the evaluation unit 116 togenerate the frequency distributions, as discussed above. Alternatively,the frequency distributions or information associated therewith (e.g.,frequency peaks and associated magnitudes) may be stored directly forcomparison. In some embodiments, the first and second frequencydistributions may be frequency-domain representations of the PVP signalsfrom the sensor 112 for time periods of fixed duration beginning attimes separated by a predetermined interval. For example, the method 700may be implemented on a rolling basis (i.e., periodically or when newPVP data becomes available) during real-time monitoring of the patient102 by comparing the magnitudes of frequency peaks (F_(N)) of frequencydistributions generated for first and second evaluation windows duringpatient monitoring. The first and second periods may be partiallyoverlapping, adjacent in time, or separated by an intervening period.

The evaluation unit 116 may next identify one or more peaks of interestfor determining the patient status metrics (block 706). The peaks ofinterest may be identified in either or both of the first and secondfrequency distributions. In some instances, the one or more peaks ofinterested may be determined based upon peaks (P_(N)) in a baselinefrequency distribution generated for the patient 102, which may be thefirst frequency distribution or an additional prior frequencydistribution. The baseline frequency distribution may, for example, bedetermined prior to scheduled surgery to establish a baseline for laterpatient status monitoring. The peaks of interest may be identified basedupon the associated frequencies (F_(N)), such as by identifying therespiratory frequency (F₀) or the heart rate frequency (F₁). In someembodiments, the peaks of interests may include a plurality of suchpeaks, such as the peaks (P₂) and (P₃) associated with the firstharmonic frequency (F₂) and the second harmonic frequency (F₃). Undersome conditions, not all peaks of interest may be identifiable in bothfrequency distributions. For example, during an acute failure of thecirculatory system, systemic vascular resistance may markedly decreaseand peaks associated with the harmonic frequencies (F₂, F₃, . . . F_(N))may not be discernible. Thus, the peaks associated with the harmonicfrequencies (F₂, F₃, . . . F_(N)) may be identifiable in the firstfrequency distribution but not in the second frequency distribution.Nonetheless, a change in magnitude of the frequency distributions at theharmonic frequencies (F₂, F₃, . . . F_(N)) may be determined bycomparison of the first and second frequency distributions.

Based upon the identified one or more peaks of interest, the evaluationunit 116 may further determine a patient status (or a change in patientstatus) by a comparison of the first and second frequency distributions(block 708). Determining the patient status may include a comparison ofthe magnitudes associated with the same one or more frequencies (F_(N))between the first and second frequency distributions, a comparison ofthe values of a function of a plurality of magnitudes associated withfrequencies between the first and second frequency distributions (e.g.,a comparison of ratios of peak magnitudes), a comparison of frequencies(F_(N)) associated with one or more peaks (P_(N)) between the first andsecond frequency distributions (e.g., a change in the respiratoryfrequency or heart rate frequency), or a comparison of other metricsassociated with patient status. In some embodiments, the patient statusmay be determined based upon a change in a metric beyond a thresholdlevel. For example, a decrease in the magnitude associated with theheart rate frequency (F₁) in the second frequency distribution below 80%of the corresponding magnitude associated with the heart rate frequency(F₁) in the first frequency distribution may indicate hypovolemia in thepatient 102. As another example, a decrease in the ratio of themagnitude associated with the first harmonic frequency (F₂) to themagnitude associated with the heart rate frequency (F₁) between thefirst and second frequency distributions beyond a predeterminedthreshold may indicate hypervolemia or hypovolemia, depending uponwhether and how the magnitude associated with the heart rate frequency(F₁) changes. Comparisons of particular interest are discussed infurther detail elsewhere herein.

A comparison involving one or more of the harmonic frequencies (F_(H))is of particular interest regarding patient hemodynamic state or bloodvolume. Because the frequency distribution values associated withharmonic frequencies (F_(H)) are more sensitive to changes in bloodvolume than the values associated with the heart rate frequency (F₁),monitoring changes in the values associated with harmonic frequencies(F_(H)) may provide an earlier or clearer indication of patienthemodynamic state. For example, a sharp increase or decrease in themagnitude of the value of the frequency distribution associated with thefirst harmonic frequency (F₂) (or other harmonic frequency) may be morepronounced than the corresponding change in the values associated withthe heart rate frequency (F₁) in the same patient at the same time.Thus, blood volume metrics may be generated using the harmonicfrequencies (F_(H)). Such metrics may be determined as functions of theharmonic frequencies (F_(H)), ratios of the frequency values of theharmonic frequencies (F_(H)), magnitudes associated with the harmonicfrequencies (F_(H)), ratios of the magnitudes associated with theharmonic frequencies (F_(H)), or changes in any of these. Such changesmay be measured against a baseline or against a previously determinedvalue at a fixed interval in time prior to the current values. In someembodiments, the frequencies or magnitudes associated with the harmonicfrequencies (F_(H)) may be compared against other relevant values, suchas frequencies or magnitudes associated with the respiratory ratefrequency (F₀) or the heart rate frequency (F₁). For example, one ormore harmonic frequencies (F_(H)) may be normalized by comparisonagainst the heart rate frequency (F₁). Such normalized value may bedetermined as a ratio of the magnitudes and may be used as a bloodvolume metric to evaluate the hemodynamic state of the patient 102.Other similar blood volume metrics based at least in part upon thefrequency and magnitude values of the one or more harmonic frequencies(F_(H)) may be determined and used to evaluate the hemodynamic state ofthe patient 102 in various embodiments.

Once patient status has been determined, the response unit 116 maydetermine whether a response is required and cause any required responseto be implemented (block 510). This may include determining a patientcondition based upon the patient status metric. Additionally, oralternatively, the evaluation unit 118 or the response unit 116 maycause an indicator of the determined patient status to be stored orpresented via the monitor 120 (block 510). If the response unit 116determines a response is required, the response unit 116 may furtherdetermine one or more responses that are appropriate to address theidentified patient status. Such responses may include generating analarm or other warning that the patient status is abnormal, which mayinclude information regarding the patient condition. An alarm or warningmay be presented via the monitor 120 or may be communicated to anotherdevice for presentation. The alarm or warning may include arecommendation of one or more actions to take in response to the patientstatus. For example, the recommendation may include an adjustment to afluid therapy for the patient 102, which may include a recommendation toadminister one or more vasopressors or vasodilators. Such recommendationmay be determined by the response unit 116 as part of the requiredresponse. In some embodiments, this may include sending an electroniccommunication to a user device (e.g., a workstation or mobile deviceused by a physician, nurse, or technician to monitor patient condition).

The responses may similarly include controlling the fluid source 110 toadjust fluid flow to the patient 102. The fluid source 110 may becontrolled to increase or reduce the rate of fluid flow to the patient102, including starting or stopping fluid flow. In some embodiments, theresponse may include controlling the fluid source 110 (or a deviceconnected thereto) to administer one or more drugs to the patient 102.For example, the fluid source 110 may be controlled to administer one ormore vasopressors or vasodilators in a fluid delivered to the peripheralvein 108 via the IV tube 104 and venous access device 106. Where thefluid source 110 includes a pump, the response may include controllingthe operation of the pump, such as by increasing or decreasing pumpspeed, flow rate, or mode of operation, as well as starting or stoppingthe pump. In some embodiments, the fluid source 110 may be controlled toadminister a quantity of a drug to the patient 102 via the fluid. Forexample, the fluid source 110 may be controlled to add a quantity of thedrug to the fluid. Additional embodiments of specific analysis andresponse methods utilizing the PIVA system 100 are further describedelsewhere herein in greater detail.

The PIVA Module

The PIVA system 100 may perform several signal filtering and signalprocessing steps (e.g., to remove the noise artifacts from aphysiological signal, to perform FFT on a physiological signal, tocalculate the PIVA Score, via the equation previously disclosed herein,as a corollary to pulmonary capillary wedge pressure, and other relatedfunctions). In an embodiment, the PIVA system 100 performs these steps,and others, via the PIVA module 800. Although the PIVA module 800 isdescribed with reference to the block diagram illustrated in FIG. 8, itwill be appreciated that many other configurations and methods ofperforming the acts associated with PIVA module 800 may be used. Forexample, the order of some of the blocks may be changed, certain blocksmay be combined with other blocks, and some of the blocks described maybe optional.

As illustrated in FIG. 8, the PIVA module 800 includes a noise module802, a signal quality index module 804, a pulse rate module 806, an FFTmodule 808, and a respiratory rate module 810.

The PIVA module 800 receives at least one input. For example, the PIVAmodule 800 may receive a digital signal from an analog-digitalconverter. The digital signal may be representative of a patientphysiological parameter, such as a patient's peripheral intravenouspressure. It should be appreciated that many other physiologicalparameters are contemplated, such as other invasive venous pressures,invasive arterial pressures, noninvasive venous pressure, noninvasivearterial pressures, and other similar parameters. In an example, thedigital signal is derived from a medical device, such as a pressuretransducer that is in fluid communication with the patient's vein.

Likewise, the PIVA module 800 delivers outputs. For example, the PIVAmodule 800 may output a signal quality index (SQI) related to the PIVAsystem 100, a respiratory rate (RR) of the patient, a pulse rate (PR) ofthe patient, and a PIVA Score of the patient.

Noise Module

Responsive to receiving the digital signal, the PIVA module 800 mayperform filtering and processing. In an embodiment, the digital signalis processed via noise module 802 to eliminate noise artifacts, such asthose associated with the operation of a pump. For example, the noisemodule 802 may perform forward-backward slope calculations to identifysegments of the digital signal where there is noise. In an embodiment,noise module 802 performs several processing steps to eliminate noiseartifacts from a signal. In an embodiment, processing includes cascadedstack processing. This may advantageously provide for real-timeprocessing and efficient decimation of recurrent feature calculations,block processes, filtering, and the like.

More specifically, the noise module 802 may evaluate the digital signal,identify a point where the positive slope of the signal is greater thana particular threshold (e.g., a signal spike), and characterize thisportion of the digital signal as a noise start point. This may generallybe characterized as slope based burst detection. Similarly, the noisemodule 802 may evaluate the digital signal, identify a point where thenegative slope of the digital signal is less than a particular threshold(e.g., a signal drop) and characterize this portion of the signal as anoise end point. Slopes may be calculated by taking the derivative ofthe digital signal.

In an example, noise module 802 implements a sliding window stack sizethat is sufficient for local parameter estimation (e.g., for real-timeprocessing). Noise module 802 determines the slope window size on eachside of a peak within a particular stack (e.g., peaks typicallyassociated with signal noise). For example, to calculate the slopes:

ForwardSlope=S{X[p-wdex]−X[p]}/(p-wdex)

BackwardSlope=S{X[p]−X[p-wdex]}(p-wdex)

Preferably, spacing between slope windows is tested for a wide range ofpump rates. Noise module 802 may also calculate a symmetry point betweenthe forward and backward slopes. The symmetry point may infer thepeak-noise location. In an embodiment, high slope and/or high amplitudenoise is detected.

The slope based burst detection is an adaptive input signal conditioningprocess, which provides for real-time noise cancellation. For example,noise module 802 identifies a noise-start and a noise-stop time, removesthe signal between the noise-start time and noise-stop time (e.g.,concatenates the signal). In other words, once a noise segment isidentified (e.g., the signal portion between the noise start point andthe noise end point), the noise module 802 may delete the segment fromthe digital signal (e.g., to produce a concatenated or segmentedsignal).

Likewise, for example, noise module 802 may also perform mirror-matchedfiltering to fill in gaps of the concatenated signal. More specifically,the signal range between the sign-adjusted forward and backward slopesgreater than a threshold (e.g., the signal noise region) is replaced bya mirror image of the symmetrically split adjacent regions. In oneembodiment, mirror-matched filtering involves filling in each gap fromthe front (e.g., from the noise end point) and the back (e.g., from thenoise start point). In a different embodiment, mirror-matched filteringinvolves filling in the gap using prior digital signal data, which isstored in a memory (e.g., buffer memory). For example, noise module 802retrieves buffer stack memory and fills synthetic data from the forwardand/or reverse direction of the signal. In an embodiment, buffer andwindow size are optimized for pump rates from 25 Hz to 250 Hz.

Processing performed by noise module 802, including slope based burstdetection and subsequent mirror matching, advantageously eliminatesnoise artifacts from signals. For example, with pump rates up to 250 mLper hour, noise module 802 has at least 0.74 seconds between pumpingintervals; this is necessary for patients with low pulse rate to acquireappropriate signals. Preferably, the end result is a cleaned signal thathas eliminated noise artifacts. After the noise module 802, the PIVAmodule 800 may perform additional processing on the cleaned signal.

Signal Quality Index Module

In an embodiment, the cleaned signal may be processed via signal qualityindex module 804 to obtain the SQI related to the PIVA system 100. Forexample, the signal quality index module 804 may include autocorrelationof the cleaned signal (e.g., the waveform), which may includedetermination of both zero-crossing mean, and standard deviation of zerocrossings. Zero crossings analysis may advantageously be used tocalculate SQI. Responsive to processing via the signal quality indexmodule 804, the PIVA module 800 may output the SQI.

More specifically, determining signal quality includes analyzing theautocorrelation of the signal. Autocorrelation may include putting theraw digital signal on top of itself (e.g., the raw digital signal on topof the cleaned signal). When there is a statistical spread in zerocrossings, approximately the same as the zero crossing rate, the signalmay be an unusable signal. For example, when the standard deviation ofthe zero crossings is similar to the number of zero crossing events, thesignal may be unusable. To calculate Signal Quality:

ZCSD=Autozerocross−zerocrossSD

Signal Quality=sqrt(abs(ZCSD)/(autozerocross+zerocrossSD))

This calculated signal quality value may be displayed as a signalquality percentage and delivered as SQI.

In an embodiment, if the signal quality is determined to be “low”quality, a monitor in communication with PIVA module 800 will display aspecific graphical user interface. For example, the monitor may indicate“Poor Signal Quality.” Similarly, the monitor may include signal qualitytroubleshooting recommendations. For example, the monitor may suggest to(1) check patient status, (2) check IV catheter for displacement, air,and kinks, (3) check pump rate to ensure it is below 250 mL per hour,(4) check for patient movement, (5) identify that the device may not becompatible for use with more than one infusion pump, and (6) flush andconfirm that the IV catheter draws back.

Pulse Rate Module

In a related embodiment, the cleaned signal may be processed via pulserate module 806 to obtain the PR of the patient. For example, the pulserate module 806 may determine top spectral peaks of the cleaned signalusing two-sided slope detection. In an example embodiment, two-sidedslope detection is a form of band pass filtering (e.g., high pass and/orlow pass filters) implemented in either hardware or software. Responsiveto processing via the pulse rate module 806, the PIVA module 800 mayoutput the PR.

More specifically, processing includes cascaded stack processing. Thismay advantageously provide for real-time processing and efficientdecimation of recurrent feature calculations, block processes,filtering, and the like.

In an embodiment, pulse rate module 806 implements autocorrelationprocessing for periodicity determination to compute pulse rate (alsoreferred to herein as heart rate or HR). For example, pulse rate module806 uses an 8192 sample block size, which may also be the stack buffersize that is processed in reverse order for correct periodicityfeatures. Pulse rate module 806 may implement selectable overlappingintervals. As an example, the default interval may be a one secondinterval with 500 samples. Pulse rate module 806 may computeautocorrelation for lags (e.g., 0 to 4000, related to periodicities upto 8 seconds). Pulse rate module 806 may compute peak-associated lags(e.g., 17 peak-associated lags), using forward and backward slopedetection as previously described. Pulse rate module 806 may filterzero-crossing periods and standard deviations. Pulse rate module 806 maycompute filtered mean-spacing between sub-harmonics. Pulse rate module806 may compute an HR estimate.

In an embodiment, pulse rate module 806 implements spectral processing(FFT) to determine HR. For example, pulse rate module 806 uses a 8192point block size, which may preferably include no window function. Pulserate module 806 may determine spectral peaks by forward and backwardslope technique. Pulse rate module 806 may use the zeroth harmonic as apartial HR estimate. The spectral magnitude associated peaks, which areindependent of autocorrelation inferred repetition rates, areaccordingly identified. Spectral magnitude peak identification may beused to calculate respiratory rate or pulse rate (e.g., via one discretepeak) as well as volume index or patient fluid status (e.g., viamultiple peaks). Discussion of spectral magnitude peak identification isincluded in the FFT Module section below.

In a related embodiment, pulse rate module 806 implements FFT to refineHRs previously determined via autocorrelation. In this embodiment, theHR initially calculated via autocorrelation is a partial HR estimate.

In another embodiment, pulse rate module 806 further computes heart ratevariability (HRV) and HRV variability. For example, because pulse ratemodule 806 is performing peak detection over sliding windows, pulse ratemodule 806 can determine how data changes or varies, and thus determineHRV and HRV variability.

FFT Module

In a related embodiment, the cleaned signal may be processed via FFTmodule 808 to obtain the PIVA Score of the patient. For example, the FFTmodule 808 may perform spectral analysis on the cleaned signal to obtainmagnitudes. These FFT magnitude spectra may be used to calculate a PIVAScore (as described in greater detail below). Responsive to processingvia the FFT module 808, the PIVA module 800 may output the PIVA Score.

More specifically, FFT module 808 is used to identify spectral magnitudepeaks, which are subsequently used to calculate volume index (e.g.,multiple peaks). In an embodiment, processing includes cascaded stackprocessing. This may advantageously provide for real-time processing andefficient decimation of recurrent feature calculations, block processes,filtering, and the like.

FFT module 808 implements spectral processing to identify spectralmagnitude peaks. In an embodiment, identification of the individualmagnitude peaks includes: utilizing the max found change in forwardbackward slope that is assisted by the guidance of the autocorrelationpulse rate, the magnitude peaks of the Fourier transformation are found.

In an embodiment, the equation to calculate PIVA Score is represented bythe following:

PIVA Score=c ₃ tan h(g ₀ +g ₁ mag ^(f1) +g ₂ mag ^(f2) +g ₃ mag^(f3)/2)+c ₂ tan h(h ₀ +h ₁ mag ^(f1) +h ₂ mag ^(f2) +h ₃ mag ^(f3)/2)+c₁ tan h(i ₀ +i ₁ mag ^(f1) +i ₂ mag ^(f2) +i ₃ mag ^(f3)/2)+c ₀

Each of c₀, c₁, c₂, c₃, g₀, g₁, g₂, g₃, h₀, h₁, h₂, h₃, i₀, i₁, i₂, andi₃ are constants. Each of mag^(f1), mag^(f2), and mag^(f3) representsthe individual magnitudes of each of the respective frequencies (e.g.,F₁, F₂, F₃). These magnitudes are also commonly referred to herein aspeaks of frequencies. For example, mag^(f1) may also be referred to aspeak P₁ herein, associated with heart rate frequency F₁. Similarly, forexample, mag^(f2) may also be referred to as peak P₂ herein, associatedwith first harmonic frequency F₂. Similarly, for example, mag^(f3) mayalso be referred to as peak P₃ herein, associated with second harmonicfrequency F₃. For example, and with reference to FIG. 4B, P₁, referredto in the PIVA Equation as mag^(f1), is the magnitude of the heart ratefrequency (F₁), P₂, referred to in the PIVA Equation as mag^(f2), is themagnitude of a first harmonic frequency (F₂), and P₃, referred to in thePIVA Equation as mag^(f3), is the magnitude of a first harmonicfrequency (F₃)

Additional ways to determine the relationship between PIVA Score andpatients' pulmonary capillary wedge pressure include the fitting of datawas evolutionary algorithms to optimize a low complexity and low errorsolution as well as neural network mapping of the data with a trainingand validation set using nodes of hyperbolic tangential functions tocreate non-linear relationships between values.

In a related embodiment, FFT module 808 performs an algorithmic approachto calculating volume index. For example, FFT module 808 performs aninitial least squares approach to analyze the individual magnitudes(e.g., F₁, F₂, F₃, etc.) and subsequently calculates a best-fit forvolume index. The best-fit for volume index can, alternatively, becharacterized as a best-fit for pulmonary capillary wedge pressure.Responsive to generating the best-fit, the FFT module 808 may use thebest-fit for subsequent iterations to calculate volume index. In thisexample, subsequent iterations may allow for additional calculations ofthe PIVA score.

Respiratory Rate Module

In an embodiment, the cleaned signal may also be processed viarespiratory rate module 810 to obtain the RR of the patient. Forexample, the respiratory rate module 810 may filter the cleaned signalthrough a high-pass filter. The respiratory rate module may furtherperform recursive discrete analysis (e.g., sin( )+cos ( ) andcomputation of related ArcTan (y/x) to determine RR. Responsive toprocessing via the respiratory rate module 810, the PIVA module 800 mayoutput the RR.

Determining respiratory rate may include using a digital linear FMdiscriminator based on differential phase angle filtering. Prior to thisdetermination, pulse rate is calculated, as described above. The pulserate data is then replicated. The respiratory rate module 810 applies adigital high-pass filter to the signal. For example, the high-passfilter isolates the respiratory rate frequency range and permits fittingof data to extract the respiratory rate.

More particularly, the input signal is high-passed filtered, for maximumvolatility detection. The respiratory rate module 810 performs recursivefiltering of quadrature:

Cosine(2*PI*n*k) and

Sin(2*PI*n*k)

and then calculates the filtered ArcTan( ) of filtered quadrature terms.The respiratory rate module 810 computes the derivative of the filteredArcTan( ) angle. In an embodiment, the respiratory rate module 810further performs light filtering of the derivative of the filteredArcTan( ) angle. The respiratory rate module 810 may then estimatedominant baseband frequency. Multiplying the estimation by 60 provides arespiration rate on a per minute basis.

In a different embodiment, the RR of the patient is determined directlyvia the FFT signal. For example, as previously stated and with referenceto FIG. 4B, under ordinary conditions, the peak (P₀) with the lowestfrequency (F₀) corresponds to the respiration rate of the patient 102.Likewise, the peak (P₁) with the next-lowest frequency (F₁) correspondsto the heart rate of the patient 102. Thus, the RR (and the HR) of thepatient can be readily determined directly via the magnitudes of therespective peaks: P₀ and P₁.

The PIVA System

FIG. 9 illustrates a block diagram of an exemplary PIVA system 900,including the PIVA module 800 previously described herein. In additionto PIVA module 800, PIVA system 900 may include processor 902 and memory904, running on PIVA module 800. For example, PIVA module 800 mayinclude one or more physical processors 902 communicatively coupled toone or more memory devices 904.

Physical processor, such as processor 902, refers to a device capable ofexecuting instructions encoding arithmetic, logical, and/or I/Ooperations. In one illustrative example, a processor may follow VonNeumann architectural model and may include an arithmetic logic unit(ALU), a control unit, and a plurality of registers. In an example, aprocessor may be a single core processor, which is typically capable ofexecuting one instruction at a time (or process a single pipeline ofinstructions), or a multi-core processor, which may simultaneouslyexecute multiple instructions. In another example, a processor may beimplemented as a single integrated circuit, two or more integratedcircuits, or may be a component of a multi-chip module (e.g., in whichindividual microprocessor dies are included in a single integratedcircuit package and hence share a single socket). A processor may alsobe referred to as a central processing unit (CPU). Memory device, suchas memory device 904, refers to a volatile or non-volatile memorydevice, such as RAM, ROM, EEPROM, or any other device capable of storingdata. Local connections, including the connections between processor 902and memory device 904, may be provided by one or more local buses ofsuitable architecture, for example, peripheral component interconnect(PCI).

Likewise, PIVA system 900 may include sensor 906 and monitor 908. Forexample, PIVA module 800 may be in communication with each of sensor 906and monitor 908. Communication may be wired and/or wireless (e.g., WiFi,Bluetooth, and other related wireless protocols). In an example, sensor906 is the pressure sensor 112 described in greater detail above. In anexample, monitor 908 is the monitor 120 described in greater detailabove. In an embodiment, PIVA module 800 is physically located withinmonitor 908.

Likewise, PIVA system 900 may include database 910 and cloud 912. Forexample, PIVA module 800 may be in communication with each of database910 and cloud 912. Communication may be wired and/or wireless (e.g.,WiFi, Bluetooth, and other related wireless protocols). In an example,database 910 includes electronic medical records stored on a hospitalnetwork. In an example, cloud 912 includes a remote storage location,which may be used to store physiological data and/or device information(e.g., PIVA module 800 performance statistics, software updates, andother related information).

In an embodiment, the PIVA system 900 displays an updated volume indexvia monitor 908 every 60 seconds. Preferably, the PIVA Score used tocompute the volume index has an agreement with pulmonary capillary wedgepressure of ±8 mmHg with limits of agreement of 95% confidence interval.

In an embodiment, the PIVA system 900 displays an updated pulse rate viamonitor 908 every 10 seconds. Preferably, the pulse rate has anagreement with the heart rate of ±10 beats per minute with limits ofagreement of 95% confidence interval.

In an embodiment, the PIVA system 900 displays an updated respirationrate via monitor 908 every 10 seconds. Preferably, the respiration ratehas an agreement with the respiratory rate of ±5 breaths per minute withlimits of agreement of 95% confidence interval.

In an embodiment, the PIVA system 900 operates in conjunction with anexternal medical device. For example, the PIVA system 900 operates inconjunction with an infusion pump operating at rates of 0 to 250 mL perhour. In a related embodiment, the PIVA system 900 utilizes noisecancellation (e.g., via noise module 802) to remove the pump signal fromthe detected waveform (e.g., the digital signal).

In an embodiment, the PIVA system 900 displays the volume index (e.g.,PIVA Score) the pulse rate, and the respiratory rate when the signalquality is adequate. For example, signal quality is adequate when thesignal quality index indicates that signal quality is adequate. Ifsignal quality is inadequate, the PIVA system 900 may indicate that thesignal is of “low” quality and/or cease displaying physiological values(e.g., PR, RR, PIVA Score, and other related physiological values) solong as the signal quality remains inadequate.

The PIVA system 900 may include other additional features. In anembodiment, the PIVA system 900 includes a power supply. The powersupply may be wired to an external source and/or may have internal power(e.g., a Li-ion battery). In an embodiment, the PIVA system 900 includesone or more speakers (e.g., a primary speaker and a backup speaker). Thespeakers may be configured to sound alarms if necessary.

FIG. 10 illustrates another example of signal processing via process1000. In various embodiments, any of PIVA system 100, PIVA system 900,and master controller 1009 (as detailed below) may perform process 1000.In an embodiment, process 1000 may be implemented in conjunction withprocess 800. In a different embodiment, example 1000 is an individualprocess, distinct from process 800. Although the process 1000 isdescribed with reference to the block diagram illustrated in FIG. 10, itwill be appreciated that many other configurations and methods ofperforming the acts associated with process 1000 may be used. Forexample, the order of some of the blocks may be changed, certain blocksmay be combined with other blocks, and some of the blocks described maybe optional.

As illustrated in FIG. 10, process 1000 may include several individualfunctions, including interference cancellation logic function 1002,frequency magnitude detection function 1004, pulse rate detectionfunction 1006, and respiratory rate detection function 1008. Each ofthese functions may be performed by or operate with the mastercontroller 1009 (e.g., a processor).

The interference cancellation logic function 1002 may include a sensorinput (block 1010). For example, a sensor input (e.g., a pressuretransducer signal) at a particular frequency (e.g., 500 Hz) may bereceived as the sensor input. The sensor input may be an analog and/or adigital signal. A 149 point FIR filter output (e.g., a low-pass filter)may be added to a slope array (block 1012). For example, the filter maybe added to the slope array that is representative of the digital signalreceived as the sensor input. In an example embodiment, the interferencecancellation logic function 1002 includes a low-pass convolution filter,to further improve the signal. Forward and backward slope detection maybe performed (block 1014). The interference cancellation logic functionmay determine if the forward/backward slope exists (block 1016). If aforward/backward slope exists, detected data points in the slopedetection array are removed (block 1018), missing values are filled infrom the point before removal (block 1022), and a cubic-fit is appliedto remove discontinuity (block 1024). Alternatively, if theforward/backward slope does not exist, input data is used as the value(block 1020).

The current point (e.g., current point of the signal) is compared to ahistogram, and rejected if the current point is outside the confidencebounds (block 1026). The data is “smoothed” to a cubic fit equation(block 1028). For example, a low pass filter (e.g., 16 Hz) may beimplemented. The output of the filtering step is added to anautocorrelation array, and the sample is shifted by one (block 1030). Inan example, the process repeats with the 149 point FIR filter outputbeing added to the slope array (block 1012). In a different example, theprocess 1000 continues on to the next function.

The frequency magnitude detection function 1004 includes determining ifthe sample count is greater than an FFT trigger value (block 1032).

If the sample count is greater than the FFT trigger value (block 1032),a FFT of the most recent 8192 points is performed (block 1034). Themagnitude of the FFT output is calculated (block 1036). The maximum peakis identified to estimate the pulse rate (block 1038). Pulse rate iscalculated (block 1040). A peak search is conducted for the harmonics ofthe first frequency (e.g., F₁) (block 1042). A spectral magnitudecalculation is performed from the pulse rate estimation (block 1044). APIVA Volume Index (e.g., PIVA Score) is calculated (block 1046) and theFFT trigger value is updated (block 1048). The PIVA Volume Index may besent to the master controller 1009. Because the PIVA Volume Index iscalculated, the frequency magnitude detection function 1004 may also begenerally characterized as a PIVA Score function.

If the sample count is not greater than the FFT trigger value (block1032), the process 1000 continues on to the next function. Likewise,responsive to performing the spectral magnitude calculation from thepulse rate estimation (block 1044), the process 1000 may continue on tothe next function.

The pulse rate detection function 1006 includes determining if thesample count is greater than an autocorrelation trigger value (block1050).

If the sample count is greater than the autocorrelation trigger value(block 1050), an inverse FFT magnitude of the most recent 8192 points isperformed (block 1052). In an example, inverse FFT magnitude providesthe time domain signal (e.g., for autocorrelation). The real output isscaled to the square-root of the magnitude (block 1054). The minimum,maximum, and mean of autocorrelation are identified (block 1056). Acubic fit of 4000 points is performed (block 1058). Forward/backwardslope calculations for minimum and maximum slope pairs are performed(block 1060). Pulse rate is calculated (block 1062). In an example,calculated pulse rate is equal to the number of slope pairs. Signalquality index (SQI) is assessed, to determine if SQI is greater than aparticular threshold (e.g., SQI>70). If SQI is greater than thethreshold (block 1064), a weighted average of pulse rates is calculated(block 1066) and the autocorrelation trigger value is updated (block1068). The calculated pulse rate may be sent to the master controller1009. In an example, calculating the weighted average of pulse rates(block 1066) includes receiving input of the pulse rate calculated bythe frequency magnitude detection function 1004 (block 1040).

If the sample count is not greater than the autocorrelation triggervalue (block 1050), the process 1000 continues on to the next function.

The respiratory rate detection function 1008 includes determining if thesample count is greater than a respiratory rate trigger value (block1070). If the sample count is not greater than the respiratory ratetrigger value, an envelope of zero crossings is calculated (block 1072).For example, an envelope of zero crossings is scaled and normalized aslog₁₀ of autocorrelation. The envelope is compared to templates ofrespiratory rate loaded into RAM (block 1074). Signal quality isassessed, to determine if SQI is greater than the particular threshold(e.g., SQI>70). If SQI is greater than the threshold (block 1076),respiratory rate is calculated (block 1078) and the respiratory ratetrigger value is updated (block 1080). The calculated respiratory ratemay be sent to the master controller 1009.

SQI is calculated using zero crossing statistics derived from theautocorrelation (e.g., the number of zero crossing events and standarddeviation of zero crossing events) (block 1082). For example,calculating SQI (e.g., at block 1064 or at block 1076) may take intoaccount pulse rate with respect to the number of slope pairs calculatedby the pulse rate detection function 1006 (block 1062). The rollingaverage of SQIs missed over time is also calculated (block 1084). If theSQI rolling average trigger is reached (block 1086), an SQI error issent to the master controller 1009. Likewise, if the SQI rolling averagetrigger is not reached (block 1086), an SQI no error is sent to themaster controller 1009.

Assessment of Patient Gait, Seizure, Activity, and Related Biometrics

As previously identified with respect to FIG. 1E, the exemplary PIVAsystem 100 may further include one or more additional sensors 150. Theseone or more additional sensors 150 may be useful, for example, tocalculate other patient variables (e.g., besides PIVA Score).

More particularly, in some embodiments, the patient status metric may bedirected to aspects of a patient condition, such as a patient bodyposition or movement. Thus, information previously monitoredunsystematically through observations of nurses or physicians mayinstead be monitored using a PVP signal on an ongoing basis. Suchmonitoring may include determining one or a plurality of patient statusmetrics associated with a position or movement of the patient, forexample. In addition to patient position metrics or patient movementmetrics, analysis of a PVP signal may be used to generate a plurality ofpatient position metrics, patient gait metrics, patient limp metrics,patient fall metrics, patient seizure metrics, other patient movementmetrics, patient blood volume metrics, patient vascular responsemetrics, patient respiratory metrics, or other similar metricsassociated with patient conditions described herein. In someembodiments, the PVP signal may be analyzed to generate primary patientmetrics, such as a pulse rate, a pulse pressure, a respiratory rate, ora respiratory depth. Thus, in some embodiments, a plurality of patientstatus metrics may be continuously monitored solely based uponmeasurements related to PVP, without additional sensors or other typesof measurements. By monitoring various patient status metrics using PIVAanalysis of a PVP signal, the methods and systems described herein avoidthe complexity, redundancy, and incompatibility of existing systems,while enabling metric-based monitoring of additional patient conditionspreviously monitored only through human observation.

In yet further embodiments, time-domain analysis may additionally oralternatively be performed to evaluate the PVP signal. The PVP signalgenerated by the pressure sensor 112 may be analyzed in the time domainor in both the time and frequency domains to determine patient status orto generate patient status metrics, such as those discussed above. Thismay include assessing a change in pressure signal due to fluid movementwithin the IV tube caused by patient movement impacting the pressuretransducer in regular patterns to determine patient movement or gait.For example, the impact associated with patient gait may generate awater hammer within the IV tube 104, which may be identified by theevaluation unit 118 as a pattern of high and low pressure observationsin the PVP signal. As another example, a measure of signal volatility orvariance may be generated to identify seizures, where variance in themeasured PVP signal will increase sharply due to pressure changes frompatient movements. Thus, the analysis of the PVP signal from the sensor112 may include identification of recurring or non-recurring patterns,which may be analyzed in either the time domain (e.g., by patternrecognition or identification of sudden changes in pressure) or in thefrequency domain (e.g., by analysis of frequencies or magnitudesassociated with local peaks in the frequency-domain representation ofthe PVP signal).

FIG. 11 illustrates a flow diagram of an exemplary patient monitoringmethod 1100 using patient PVP to determine and respond to patient statusmetrics. The exemplary patient monitoring method 1100 obtains PVP datafor the patient 102 and analyzes the data using the PIVA system 100. Theexemplary method 1100 may be performed by one or more software orhardware modules of the analysis component 114 using an electronicpressure signal from the pressure sensor 112, which may includegenerating the electronic pressure signal by the pressure sensor 112 insome embodiments. Likewise, the exemplary method 1100 may includedetermining and implementing responses to one or more patient metrics,which may include presenting alarms or controlling medical devices totreat a patient condition (e.g., controlling operation of a pump orother fluid source connected to the patient's circulatory system).

The exemplary method 1100 begins by monitoring a PVP signal for thepatient 102 (block 1102). This may include generating a time-domain PVPsignal by measuring a physical phenomenon associated with the PVP of thepatient 102 using a PVP sensor (such as the pressure sensor 112 orsimilar sensors) via an IV tube 104. Alternatively, this may includereceiving or accessing a continuous or discrete time-domain PVP signalfrom the PVP sensor or storage medium, which PVP signal may include rawmeasurement data from the sensor or data derived therefrom. Howeverobtained, the PVP signal may be monitored until a sufficient sampleperiod (e.g., an evaluation window) of time-domain PVP data is obtainedto enable transformation and analysis, as discussed elsewhere herein.

Once the PVP signal has been obtained, the analysis component 114 maygenerate a frequency distribution by transforming the time-domain PVPsignal to the frequency domain (block 1104). This may include applying afast Fourier transform (FFT) to the time-domain PVP signal or othertransformation techniques, as discussed elsewhere herein. As discussedelsewhere herein, the frequency distribution may be represented in anyconvenient form, including as an array or matrix storing data ofassociated frequencies and magnitudes. In some embodiments, this mayinclude determining a plurality of frequency distributions fromoverlapping or non-overlapping portions of the PVP signal (e.g., a firsthalf of the sample period and a second half of the sample period). Suchplurality of frequency distributions may then be analyzed to determineone or more patient status metrics, which may then be compared todetermine a change in patient status or condition.

The frequency distribution may then be analyzed by evaluating one ormore frequencies (F_(N)) or associated magnitudes of peaks (P_(N)) inthe frequency distribution to determine at least one patient statusmetric (block 1106). In some embodiments, this may include identifyingthe one or more frequencies (F_(N)) associated with local maxima of thefrequency-domain PVP signal represented by the frequency distribution,as discussed elsewhere herein. Alternatively, the frequency distributionmay include information indicating such frequencies and magnitudes.Based upon the identified frequencies (F_(N)), one or more patientmetrics associated with a position or movement of the patient 102 may bedetermined by analyzing the frequencies or associated magnitudes. Insome embodiments, a patient status metric may be determined based uponchanges to a frequency or magnitude. Such changes may be determined bycomparison against a previously measured frequency distribution (e.g., afrequency distribution for PVP measured during an immediately precedingsample period) or against a baseline frequency distribution (e.g., afrequency distribution for PVP measured while the condition of thepatient was known, such as immediately prior to surgery). Such baselinefrequency distributions may include information regarding one or morebaseline frequencies and associated baseline magnitudes associated withknown patient condition. The one or more patient status metrics mayinclude patient position metrics, patient movement metrics, or primarypatient metrics.

Primary patient metrics provide basic information regarding the patient102 and may be used directly or indirectly to monitor the patient'scondition. Thus, primary patient metrics may include informationregarding patient circulatory and respiratory status, such as a pulserate, a pulse pressure, a respiratory rate, or a respiratory depth. Arespiratory rate or pulse rate may be determined by simply identifyingthe respiratory frequency (F₀) or the heart rate frequency (F₁).Harmonic frequencies (F₂, F₃, . . . F_(N)) of the heart rate frequency(F₁) may be used to identify or confirm the heart rate frequency (F₁),which may further be used to identify the respiratory frequency (F₀).Magnitudes associated with the respiratory frequency (F₀) or the heartrate frequency (F₁) may be used to determine respiratory depth or pulsepressure. In some embodiments, pulse pressure may be determined byconverting the magnitude of the peak (P₁) associated with the heart ratefrequency (F₁) to a time-domain signal and determining the amplitudethereof. Similarly, respiratory depth may be determined based upon themagnitude associated with the respiratory frequency (F₀) by convertingthe magnitude of the corresponding peak (P₀) to a signal in thefrequency domain, determining the amplitude thereof, and calculating therespiratory depth based upon the amplitude. For example, the respiratorydepth may be calculated using a statistical model determined from PVPmeasurements and respiratory depth measurements (or estimates) during abaseline period. Other similar primary patient metrics may be similarlydetermined from the frequency distribution.

Patient position metrics provide information regarding the posture orrelative position of the parts of the patient's body. Thus, a patientposition metric may indicate whether the patient 102 is in an uprightposition or recumbent position. Such relative position informationregarding whether the patient is standing, sitting, or lying flat may bedetermined from the absolute or relative magnitudes of the one or morefrequencies (F_(N)). For example, a patient position metric may indicatea change in an absolute magnitude of the peak (P₁) associated with theheart rate frequency (F₁) compared against a previously measuredmagnitude of the same peak (P₁) associated with a known patient position(e.g., sitting). Thus, an increased magnitude may indicate an increasedpressure in the patient's peripheral venous system, which may indicatethe patient 102 is lying flat. In some embodiments, informationregarding the location of the venous access device 106 (e.g., in apatient hand, arm, or leg) may be recorded when the IV tube 104 isconnected and subsequently used in the determination of the patientposition metric. In further embodiments, ratios or combinations ofmagnitudes may be used, such as the ratio of the magnitudes of the peak(P₁) associated with the heart rate frequency (F₁) and one or more ofthe peaks (P₂, P₃, . . . P_(N)) associated with harmonic frequencies(F₂, F₃, . . . F_(N)) thereof.

Patient movement metrics provide information regarding the occurrence ofpatient movement, the type of patient movement, or the condition of thepatient based upon movement. Such patient movement metrics may provideinformation regarding sudden patient movements, muscle spasms, patientgait, limping, stability, falls, or seizures. A patient gait metric maybe determined based upon the frequency distribution associated with PVPfor an ambulatory patient. A gait frequency (F_(G)) associated with thepatient's gait while walking may be identified from the frequencydistribution. In some embodiments, this may include first identifyingthe respiratory frequency (F₀) or the heart rate frequency (F₁) toidentify the gait frequency (F_(G)) based upon a peak (P_(G)) in thefrequency distribution that is below the heart rate frequency (F₁) andis not the respiratory frequency (F₀). In further embodiments, the heartrate frequency (F₁) may first be identified, such as by magnitude orharmonic frequencies (F₂, F₃, . . . F_(N)). Identifying the gaitfrequency (F_(G)) may also include a comparison of the relativemagnitudes associated with the respiratory frequency (F₀) and the gaitfrequency (F_(G)) to identify the gait frequency (F_(G)) as beingassociated with a lower magnitude. In some embodiments, the respiratoryfrequency (F₀) may be identified based upon proximity to the respiratoryfrequency (F₀) of a previous time period (e.g., a prior sample period orevaluation window). The frequency and magnitude of the gait frequency(F_(G)) may be further evaluated to determine information regarding thepatient's gait, such as rate, regularity, limping, or stability.

In some embodiments, a secondary gait frequency (F_(G2)) may also beidentified based upon a corresponding peak (P_(G2)) in the frequencydistribution. Such secondary gait frequency (F_(G2)) may be used indetermining the patient gait metric or a separate patient limp metric.The secondary gait frequency (F_(G2)) may be identified as a fraction ormultiple of the gait frequency (F_(G)). Alternatively, the secondarygait frequency (F_(G2)) may be identified as beginning and endingconcurrently with the gait frequency (F_(G)) across multiple frequencydistributions associated with a time series of sample periods. Asanother alternative, the secondary gait frequency (F_(G2)) may beidentified as beginning associated with a corresponding peak (P_(G2)) inthe frequency distribution that is not associated with the respiratoryfrequency (F₀), the heart rate frequency (F₁), a harmonic frequency (F₂,F₃, . . . F_(N)), or the gait frequency (F_(G)). In some relatedembodiments, a minimum threshold magnitude may be used to ensure thesecondary gait frequency (F_(G2)) has a sufficient magnitude relative tothe gait frequency (F_(G)), thereby eliminating from consideration minorfrequency peaks caused by noise or other phenomena not related topatient perambulation. However identified, the secondary gait frequency(F_(G2)) may be evaluated to determine the consistency of the patient'sgait, including whether the patient is limping while walking. Theregularity (i.e., fixedness of the frequency) or the magnitudeassociated with the secondary gait frequency (F_(G2)) may be evaluatedto determine a patient gait consistency metric (which may be part of thepatient gait metric) indicating whether the patient's gait is stable,unstable, normal, or abnormal (i.e., indicating a limp). For example,the consistent location of the secondary gait frequency (F_(G2)) at aninteger fraction or multiple of the gait frequency (F_(G)) may indicatea limp, whereas shifting frequency values of the secondary gaitfrequency (F_(G2)) over time may indicate instability. Similarly, alarger magnitude associated with the secondary gait frequency (F_(G2))relative to the magnitude associated with the gait frequency (F_(G)) mayindicate a more pronounced limp.

Although only one secondary gait frequency (F_(G2)) is discussed above,it should be understood that multiple secondary gait frequencies(F_(G2)) could be identified and evaluated to determine the consistencyof the patient's gait. Similarly, in some embodiments, the magnitudeassociated with the gait frequency (F_(G)) may be compared against aplurality of magnitudes associated with other frequencies within thefrequency distribution to determine the consistency of the patient'sgait. Such comparison may be made against a measure of the total oraverage magnitude across a range of frequencies of the frequencydistribution. For example, the ratio of the magnitude associated withthe gait frequency (F_(G)) to the median magnitude of the frequencydistribution may be calculated as a patient gait metric indicatingstability of an ambulatory patient. A higher ratio indicates a steadygait while the patient is walking, while a lower ratio indicatesinstability as minor variations in gait lead to relatively greatermagnitudes at other frequencies. Thus, even frequencies not associatedwith peaks (i.e., local maxima) of the frequency distribution may beevaluated in generating some patient status metrics, particularlymetrics associated with stability or instability. In some embodiments, aseparate patient stability metric may be determined based uponmagnitudes associated with a plurality of secondary gait frequencies(F_(G2)) or other frequencies, including averages (e.g., medians) acrossa range of frequencies within the frequency distribution.

Patient movement metrics may further include patient fall metricsindicating a patient has fallen. In some embodiments, the patient fallmetric may be determined as a binary metric indicating either presenceor absence of a fall. Alternatively, the patient fall metric may bedetermined as a probability of a fall based upon the frequencydistribution. In the time domain, a fall will appear as a sudden spikein measured pressure, with PVP rising quickly as the shock of impactpropagates through the circulatory system and then quickly returningapproximately to previous levels. In the frequency domain, such a spikeor pulse in the time domain signal is identifiable by a characteristicpattern of peaks and troughs. For example, the spike may be viewed asapproximating a square pulse, the characteristic frequency distributionof diminishing magnitude peaks symmetrically around frequency zero (0Hz) is well known. Thus, the PVP pulse associated with the impact when apatient falls may be identified by identifying a pattern within thefrequency distribution that is associated with a short-duration pulse inthe time-domain PVP signal. In some embodiments, the pattern may beidentified after identifying and removing peaks in the frequencydistribution associated with frequencies of interest, such as therespiratory frequency (F₀), the heart rate frequency (F₁), the harmonicfrequencies (F₂, F₃, . . . F_(N)) thereof, or the gait frequency(F_(G)). In alternative embodiments, the time-domain spike may beidentified as a large magnitude of a peak associated with a lowfrequency in the frequency distribution. As the transient pressure pulsefrom falling will be large relative to other influences on thetime-domain PVP signal, the magnitude associated with the primary peakof the frequency distribution generated thereby will also be large.Thus, a fall may be detected in some instances based upon suchmagnitude.

In further embodiments, the spike associated with a fall may further beidentified in the time-domain PVP signal, which may be advantageous inconfirming the occurrence of the fall and identifying the time of thefall. Once the time of the fall is identified, the sample periodincluding the fall may be divided into a pre-fall portion and apost-fall portion for further evaluation. In some instances, thepre-fall and post-fall portions of the sample period may be augmented bythe addition of earlier and later values of the time-domain PVP signal,respectively, to ensure sufficient time-domain PVP data for evaluationof each portion of the original sample period. The pre-fall andpost-fall portions may be separately transformed to generate pre-falland post-fall frequency distributions. Shifts in frequency or changes inmagnitude of the peaks of interest may then be evaluated to determine aseverity of the fall, which may be included in a patient fall metric.For example, a percentage increase in the heart rate frequency (F₁)following the fall may be calculated as a metric to evaluate severity ofthe fall, as the patient's body responds to the incident. Other similarchanges in frequencies or associated magnitudes may likewise bedetermined in various embodiments.

Patient movement metrics may further include patient seizure metricsindicating occurrence of a seizure. The patient seizure metrics mayinclude a ratio of the magnitude associated with the heart ratefrequency (F₁) relative to magnitudes associated with one or more otherfrequencies within the frequency distribution. For example, the ratio ofthe magnitude associated with the heart rate frequency (F₁) relative tothe average magnitude of the frequencies within a range of the frequencydistribution (e.g., from 0 Hz to 5 Hz) may be used as a patient seizuremetric to indicate how well-defined the heart rate is relative to othercomponents of the time-domain PVP signal. Although other factors mayaffect it, the ratio between the magnitude of the heart rate frequency(F₁) and the average magnitude will be less for a seizing patient thanfor a healthy patient. During a seizure, movements of the patient's bodygenerate substantial noise in the PVP signal, resulting in a generalincrease in magnitudes associated with frequencies across the frequencydistribution. If sufficiently severe, the heart rate frequency (F₁) maynot be identifiable from the surrounding noise. In further embodiments,the patient seizure metric may be determined based upon an absolutelevel of the average magnitude of the frequency distribution or theaverage (e.g., median) magnitude from a sample of a plurality offrequencies (e.g., ten or twenty frequencies). In related embodiments,the patient seizure metric may be determined based upon a comparison ofan average magnitude between frequency distributions associated withdifferent sample periods, such that a sharp increase in averagemagnitude may be indicative of a seizure.

In some embodiments, the analysis component 114 may simultaneouslymonitor a plurality of patient status metrics, such as by evaluating thefrequencies (F_(N)) or associated magnitudes of peaks (P_(N)) in thefrequency distribution. Such patient status metrics may be determinedusing the same frequency distribution for the same sample period. When acomparison between sample periods is used to generate a patient statusmetric, the same plurality of frequency distributions associated withthe same sample periods may be used. The plurality of patient statusmetrics may include metrics from within one or more of the primarypatient metrics, patient position metrics, or patient movement metricsgroups discussed above, as well as other metrics. For example, a patientseizure metric and another patient movement metric (e.g., a patient gaitmetric or a patient fall metric) may be monitored simultaneously fromthe same frequency distribution. As another example, a primary patientmetric (e.g., a pulse rate, a pulse pressure, a respiratory rate, or arespiratory depth) and a patient position metric or a patient movementmetric may be monitored simultaneously from the same frequencydistribution. As yet another example, a fall or seizure may beidentified by identifying combinations of abnormal gait metrics (e.g.,variable gait frequencies, secondary gait frequencies, or a water hammereffect) with patient stress indicators (e.g., increased heart rate orrespiratory rate). By using (explicitly or implicitly) the frequency andmagnitude information associated with the observed PVP signal, any orall of the foregoing patient status metrics may be monitored withoutrequiring the use of additional sensors beyond the PVP sensor (e.g., thepressure sensor 112).

Although the foregoing description has presented the analysis as beingperformed using frequency-domain PVP data, other embodiments mayadditionally or alternatively include other types of analysis togenerate patient status metrics, including any of the primary patientmetrics, patient position metrics, and patient movement metricsdiscussed above or combinations thereof. For example, a patient movementmetric may be determined by analyzing the PVP signal in the time domainto identify physical movement or gait of the patient by assessing thechange in pressure due to fluid movement within the IV tube (e.g., waterhammer effect) caused by patient arm movement within the gait activity,impacting the pressure sensor 112 in regular patterns. As anotherexample, a patient fall metric may be determined by identifying pressurespikes beyond a threshold magnitude in the time-domain PVP signal fromthe pressure sensor 112.

Based upon the one or more patient status metrics, the analysiscomponent 114 may determine a response to a patient condition (block1108) and implement the determined response (block 1110). For example,the response unit 116 may determine whether a response is required andcause any required response to be implemented. This may includedetermining one or more patient conditions by evaluating one or morepatient status metrics. Patient conditions may include a position (e.g.,sitting or standing), instability, limp, fall, seizure, or other similarconditions. The patient conditions may include position conditions,movement conditions, or primary conditions. For example, positionconditions may include lying, sitting, or standing, while movementconditions may include walking, unsteady walking, limping, falling, orseizing. Primary conditions may include shallow breathing,hyperventilating, not breathing, irregular breathing, normal breathing,normal heartbeat, slow heartbeat, rapid heartbeat, or irregularheartbeat. Determining each of the patient conditions may includeevaluating one or more patient status metrics. For example, determininga patient is unsteadily walking may include evaluating a patient gaitmetric to determine whether the patient is walking, then evaluating aseparate stability metric to determine the patient is unstable whilewalking. Some conditions may be determined based upon a combination ofsuch metrics. For example, determining a patient is in a normalcondition may require all monitored patient status metrics to be withinacceptable ranges.

Whether the patient condition is determined based upon the patientstatus metrics or the patient condition is implied from the value of apatient status metric, one or more responses related to one or morepatient conditions may be determined based upon the patient statusmetrics. Although some conditions may require active responses, otherconditions may simply require continued monitoring (or no response). Forexample, when all patient status metrics that have been determined forthe patient 102, the analysis component 114 may determine that theappropriate response to the normal patient condition is to continuemonitoring. In such case, the response may be implemented by generatingor obtaining additional sensor data regarding PVP and performing furtheranalysis on the additional data according to the methods describedherein. Active responses may include presenting an alarm or controllingoperation of a medical device. An alert may be generated based upon thepatient status metrics or conditions determined therefrom, which alertmay include information regarding the condition or remedial actions tobe taken. For example, an alert may indicate that a patient isunsteadily walking. A visual, audible, or tactile alarm or warning maybe presented to appropriate personnel (e.g., via the monitor 120) basedupon the alert, which may include displaying a message indicating thetype of condition or a recommended course of action. Operation of amedical device in response to the patient condition may includecontrolling the fluid source 110 to adjust fluid flow to the patient102. This may include adjusting a flow rate, starting or stopping fluidflow, adding one or more drugs to the fluid, or similar control actions,as discussed further below. In some embodiments, the analysis component114 may directly control the implementation of the response bycontrolling the fluid source 110 or the monitor 120. Alternatively, theanalysis component 114 may communicate control information to otherdevices to cause those devices to present an alert or control operationof a medical device.

In some embodiments, the patient condition or the response to thepatient condition may be determined based in part upon additional sensordata from one or more additional sensors 150. For example, a pressuresensor may generate additional sensor data indicating whether thepatient is in a bed, which may be combined with patient movement metricsto determine whether the patient is at risk to fall. If the additionalsensor data indicates patient is lying in the bed, no response beyondcontinuing monitoring may be required despite a patient stability metricindicating instability. If the additional sensor data instead indicatesthe patient is not lying in the bed, however, an alarm may be generatedto alert appropriate personnel that the patient is at risk of falling.Some embodiments may not include additional sensors 150 or may not usesensor data therefrom to determine patient conditions or responses tothe patient conditions. In such embodiments, responses may be determinedusing only the patient status metrics derived from measurements of PVPvia the pressure sensor 112.

As used in this specification, including the claims, the term “and/or”is a conjunction that is either inclusive or exclusive. Accordingly, theterm “and/or” either signifies the presence of two or more things in agroup or signifies that one selection may be made from a group ofalternatives.

The many features and advantages of the present disclosure are apparentfrom the written description, and thus, the appended claims are intendedto cover all such features and advantages of the disclosure. Further,since numerous modifications and changes will readily occur to thoseskilled in the art, the present disclosure is not limited to the exactconstruction and operation as illustrated and described. Therefore, thedescribed embodiments should be taken as illustrative and notrestrictive, and the disclosure should not be limited to the detailsgiven herein but should be defined by the following claims and theirfull scope of equivalents, whether foreseeable or unforeseeable now orin the future.

The invention is claimed as follows:
 1. A system for monitoring a patient using a measurement associated with a peripheral venous pressure (PVP) within a peripheral vein of a circulatory system of the patient while the circulatory system of the patient is connected to a pump, comprising: a PVP sensor including a transducer disposed adjacent to or connected to an intravenous (IV) tube in fluid connection with the peripheral vein and configured to generate an electronic signal associated with the PVP while the circulatory system of the patient is connected to the pump; and an evaluation unit, including a computer processor communicatively connected to the PVP sensor to receive the electronic signal and a memory storing non-transitory computer-readable instructions that, when executed by the computer processor, cause the evaluation unit to: obtain a time-domain PVP signal comprising values of an electronic signal associated with the PVP from the transducer based upon a physical phenomenon associated with the PVP of the patient over a sample period, wherein the sample period includes a plurality of time segments, including (i) one or more active time segments during which the pump is operating and (ii) one or more inactive time segments during which the pump is not operating; identify a first plurality of the values of the time-domain PVP signal associated with the one or more inactive time segments and a second plurality of the values of the time-domain PVP signal associated with the one or more active time segments, based upon evaluation of the values of the time-domain PVP signal; generate a filtered time-domain PVP signal based upon the first plurality of the values and excluding the second plurality of the values; apply a transformation to the filtered time-domain PVP signal to generate a frequency-domain PVP signal; and determine a patient status metric for the patient based upon the frequency-domain PVP signal.
 2. The system of claim 1, wherein the pump is a peristaltic IV pump.
 3. The system of claim 1, wherein the pump is configured to operate periodically, such that the one or more active time segments and the one or more inactive time segments periodically alternate.
 4. The system of claim 1, wherein the IV tube is disposed between the patient and the pump such that a part of the pump is in fluid connection with the peripheral vein of the circulatory system of the patient via the IV tube.
 5. The system of claim 4, wherein: the transducer comprises a pressure sensor disposed in fluid connection with an interior of the IV tube; and the physical phenomenon associated with the PVP is a pressure within the interior of the IV tube.
 6. The system of claim 4, wherein the instructions further cause the evaluation unit to: determine whether the patient status metric indicates a condition of the patient is abnormal; and adjust operation of the pump when the patient status metric indicates the condition of the patient is abnormal by changing a rate of flow of a fluid from the pump into the circulatory system of the patient.
 7. The system of claim 1, wherein the executable instructions that cause the evaluation unit to generate the filtered time-domain PVP signal include instructions that cause the evaluation unit to remove the one or more active time segments from the time-domain PVP signal.
 8. The system of claim 7, wherein the executable instructions further cause the evaluation unit to generate the filtered time-domain PVP signal by, for each of one or more pairs of the active time segments: identifying one or more corresponding values within both of the active time segments of the pair; and combining the active time segments of the pair by aligning the one or more corresponding values within both of the active time segments of the pair.
 9. The system of claim 1, wherein the executable instructions that cause the evaluation unit to generate the filtered time-domain PVP signal include instructions that cause the evaluation unit to: estimate a third plurality of values as substitute values for the one or more active time segments, wherein the third plurality of values are estimated based upon the first plurality of values without reference to the second plurality of values; and generate the filtered time-domain PVP signal by combining the first plurality of values for the inactive time segments and the third plurality of values for the active time segments.
 10. The system of claim 9, wherein the third plurality of values are estimated by performing at least one of regression analysis, forward-backward slope calculation, two-sided slope detection, and mirror matched filtering on at least the first plurality of values.
 11. The system of claim 1, wherein the executable instructions that cause the evaluation unit to determine the patient status metric include instructions that cause the evaluation unit to: identify a plurality of frequencies associated with local maxima of the frequency-domain PVP signal; and determine the patient status metric based at least in part upon at least one of the plurality of frequencies associated with the local maxima.
 12. The system of claim 1, wherein the patient status metric is a blood volume metric indicating one or more of the following: hypovolemia, hypervolemia, or euvolemia.
 13. A device for monitoring a patient, comprising: a peripheral venous pressure (PVP) sensor, including a transducer configured to monitor a physical phenomenon associated with a PVP within a peripheral vein of a circulatory system of the patient while the circulatory system of the patient is connected to a pump; and an evaluation unit, including a computer processor communicatively connected to the PVP sensor and a memory storing non-transitory executable instructions that, when executed by the computer processor, cause the evaluation unit to: obtain a time-domain PVP signal comprising values of an electronic signal associated with the PVP received from the transducer of the PVP sensor over a sample period, wherein the sample period includes a plurality of time segments, including (i) one or more active time segments during which the pump is operating and (ii) one or more inactive time segments during which the pump is not operating; identify a first plurality of the values of the time-domain PVP signal associated with the one or more inactive time segments and a second plurality of the values of the time-domain PVP signal associated with the one or more active time segments, based upon evaluation of the values of the time-domain PVP signal; generate a filtered time-domain PVP signal based upon the first plurality of the values and excluding the second plurality of the values; apply a transformation to the filtered time-domain PVP signal to generate a frequency-domain PVP signal; and determine a patient status metric for the patient based upon the frequency-domain PVP signal.
 14. The device of claim 13, wherein: the time-domain PVP signal comprises a first time series of discrete values; the filtered time-domain PVP signal comprises a second time series of discrete values; and the second time series contains at least one segment of a sequential plurality of values within the second time series that are equivalent to a corresponding segment of a sequential plurality of corresponding values within the first time series.
 15. The device of claim 13, wherein the executable instructions that cause the evaluation unit to generate the filtered time-domain PVP signal include instructions that cause the evaluation unit to remove the one or more active time segments from the time-domain PVP signal.
 16. The device of claim 13, wherein the executable instructions that cause the evaluation unit to generate the filtered time-domain PVP signal include instructions that cause the evaluation unit to: estimate a third plurality of values as substitute values for the one or more active time segments, wherein the third plurality of values are estimated based upon the first plurality of values without reference to the second plurality of values; and generate the filtered time-domain PVP signal by combining the first plurality of values for the inactive time segments and the third plurality of values for the active time segments.
 17. A method of monitoring a patient using a measurement associated with a peripheral venous pressure (PVP) within a peripheral vein of a circulatory system of the patient while the circulatory system of the patient is connected to a pump, comprising: monitoring, by a transducer, a physical phenomenon associated with the PVP of the patient over a sample period, wherein the sample period includes a plurality of time segments, including (i) one or more active time segments during which the pump is operating and (ii) one or more inactive time segments during which the pump is not operating; obtaining, by a processor of an evaluation unit, a time-domain PVP signal comprising values of an electronic signal associated with the PVP from the transducer based upon the monitored physical phenomenon over the sample period; identifying, by the processor of the evaluation unit, a first plurality of the values of the time-domain PVP signal associated with the one or more inactive time segments and a second plurality of the values of the time-domain PVP signal associated with the one or more active time segments, based upon evaluation of the values of the time-domain PVP signal; generating, by the processor of the evaluation unit, a filtered time-domain PVP signal based upon the first plurality of the values and excluding the second plurality of the values; applying, by the processor of the evaluation unit, a transformation to the filtered time-domain PVP signal to generate a frequency-domain PVP signal; and determining, by the processor of the evaluation unit, a patient status metric for the patient based upon the frequency-domain PVP signal.
 18. The method of claim 17, wherein generating the filtered time-domain PVP signal includes removing the one or more active time segments from the time-domain PVP signal.
 19. The method of claim 17, wherein generating the filtered time-domain PVP signal includes: estimating a third plurality of values as substitute values for the one or more active time segments, wherein the third plurality of values are estimated based upon the first plurality of values without reference to the second plurality of values; and generating the filtered time-domain PVP signal by combining the first plurality of values for the inactive time segments and the third plurality of values for the active time segments.
 20. The method of claim 17, wherein the third plurality of values are estimated by performing at least one of regression analysis, forward-backward slope calculation, two-sided slope detection, and mirror matched filtering on at least the first plurality of values. 